The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
The heterogeneity of “major depression”
Open accessAbstract
The concept of major depression, in both the ICD and the DSM, has been a flagship for mental health in general medical settings. It is the principal mental disorder emphasized to general practitioners and hospital doctors, and it has been used by governments to raise awareness of mental health issues in the population at large and in the medical community 1,2,3. It has encouraged the development of specialized forms of psychotherapy for depression, it has allowed the formation of community groups to propagate accurate information about depression, and has led to the development of computer programmes to assist self-treatment using the principles of cognitive-behavioural therapy 4,5. These are substantial achievements, but they come at a price. This is the belief that “major depression” is a homogeneous entity, and indeed that it is usually a “major” disorder. The DSM diagnosis of major depression is made when a patient has any 5 out of 9 symptoms, several of which are opposites. Thus, a patient who has psychomotor retardation, hypersomnia and gaining weight is scored as having identical symptoms as another who is agitated, sleeping badly and has weight loss. This causes real problems with research designs: for example, Lux and Kendler 6 had to group these opposite symptoms together. Even so, it was possible for them to distinguish between “cognitive” and “neurovegetative” symptoms, and show that these have different relationships to a larger set of potential validators. They concluded that their results “challenge our understanding of major depression as a homogeneous categorical entity”. Others have been able to separate the various depressive symptoms, and to compare the relative efficiency of each symptom to making the diagnosis 7. Jang et al 8 factor analysed a larger set of depressive symptom scales, and found that they could identify 14 different subscales, which had rather low intercorrelations, and very different heritabilities. Given these findings, to declare that all those satisfying the DSM criteria for the diagnosis of major depression are suffering from the same disorder seems like magical thinking. We know that many milder cases remit without specific treatment, suggesting that they are indeed homeostatic responses to life stress 9. Depression may be a toxic reaction to drugs or may result from endocrine disorders such as myxoedema or Cushing's syndrome. The depressed phase of bipolar illness may be difficult or impossible to distinguish from unipolar depression. Melancholic, atypical and psychotic forms of depression are yet other variants. Agitated depression needs to be distinguished from retarded depression when choosing the most suitable antidepressant. But even with these exclusions, there are five other forms of major depression that require a range of different responses from the clinician. The first form is depression presenting with somatic symptoms 10. Many patients with this condition may be resistant to accepting that they are depressed. They benefit from special additional measures that explain how emotional arousal and depression can cause their somatic symptoms. Such measures have been developed for many years, and have recently been elaborated 11. The second form is depression with panic attacks. While treatment of depression is the first priority in these patients, it is also important to give them advice on what to do during a panic attack, as it may take a little time before improvement in their depression stops further attacks. They need advice about not immediately leaving the environment in which the panic attack is taking place, explanations about catastrophizing thoughts and advice on helpful “self-talk”. They need to remind themselves that they have had such attacks before, and they will pass off if they calm down and remember the reassuring thoughts that run counter to the content of their thoughts during an attack. Such advice makes the attacks easier to deal with, and less likely to become still worse. The third form is depression in people with obsessional traits. People with these traits in their usual personality often develop quite severe obsessional behaviour and depressive ruminations during a depression. These symptoms may be experienced as the leading symptoms, but can be thought of as epiphenomena of their depressive illness. It is helpful to take the patient through thought-stopping techniques, distraction techniques and response prevention. The fourth form is depression accompanying known physical illnesses. These depressions are particularly poorly recognized by generalists, who typically confine themselves to the treatments for the physical illness 12. Diagnosis of these depressions is complicated by the fact that four of the “diagnostic features” of depression (fatigue, poor sleep, poor appetite and weight loss) may well be caused by the physical illness. This may generate confusion, since no clear threshold for the number of symptoms needed for a di
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Revisiting the hypothesis of Taylor and colleagues (46), one potential reason why model personalisation may provide benefits over “traditional” machine learning methods is because this allows to deal with heterogeneity, a feature that is characteristic both of depression and the way patients respond to treatment (83, 84).”
From Personalised depression forecasting using mobile sensor data and ecological momentary assessment (2022) · cites this paper as [84]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Delivering interventions for depression by using the internet: randomised controlled trial
Christensen, H. (2004) · BMJ · doi:10.1136/bmj.37945.566632.ee · Cited by 928 · cited 1×
“It has encouraged the development of specialized forms of psychotherapy for depression, it has allowed the formation of community groups to propagate accurate information about depression, and has led to the development of computer programmes to assist self-treatment using the principles of cognitive-behavioural thera…”
Proudfoot, J., Ryden, C., Everitt, B., Shapiro, D. A., Goldberg, D., Mann, A., Tylee, A., Marks, I., & Gray, J. A. (2004) · British Journal of Psychiatry · doi:10.1192/bjp.185.1.46 · Cited by 516 · cited 1×
“It has encouraged the development of specialized forms of psychotherapy for depression, it has allowed the formation of community groups to propagate accurate information about depression, and has led to the development of computer programmes to assist self-treatment using the principles of cognitive-behavioural thera…”
Self-citationRegier, D. A., Hirschfeld, R. M. A., Goodwin, F. K., Burke, J. D., Lazar, J. B., & Judd, L. L. (1988) · American Journal of Psychiatry · doi:10.1176/ajp.145.11.1351 · Cited by 367
Diagnosing DSM-IV—Part I: DSM-IV and the concept of disorder
Wakefield, J. C. (1997) · Behaviour Research and Therapy · doi:10.1016/s0005-7967(97)00018-1 · Cited by 212
Deconstructing major depression: a validation study of the DSM-IV symptomatic criteria
Lux, V., & Kendler, K. S. (2010) · Psychological Medicine · doi:10.1017/s0033291709992157 · Cited by 204
Jorm, A. F., Christensen, H. M., & Griffiths, K. M. (2005) · Australian & New Zealand Journal of Psychiatry · doi:10.1111/j.1440-1614.2006.01739.x · Cited by 169
The Defeat Depression Campaign: psychiatry in the public arena
(1997) · American Journal of Psychiatry · doi:10.1176/ajp.154.6.59 · Cited by 154
The detection and treatment of depression in the physically ill
Goldberg, D. P. (2010) · World Psychiatry · doi:10.1002/j.2051-5545.2010.tb00256.x · Cited by 105
Self-citationClassification of Anxiety and Depressive disorders: problems and solutions
Andrews, G., Anderson, T. M., Slade, T. N., & Sunderland, M. (2008) · Depression and Anxiety · doi:10.1002/da.20489 · Cited by 104
Enhanced care by generalists for functional somatic symptoms and disorders in primary care
Rosendal, M., Blankenstein, A. H., Morriss, R. K., Fink, P., Sharpe, M., & Burton, C. D. (2013) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd008142.pub2 · Cited by 83
Research works printed but not linked: 1
- Ustun TB, 1995, Mental illness in general health care
Cited by
Sorted by citations
Items 201–300 of 335
Hartnagel, L., Emden, D., Foo, J. C., Streit, F., Witt, S. H., Frank, J., Limberger, M. F., Schmitz, S. E., Gilles, M., Rietschel, M., et al. (2024) · JMIR Mental Health · doi:10.2196/64578 · Cited by 9
Zhao, K., Wang, Y., Liu, Q., Yu, Z., & Feng, W. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1485179 · Cited by 6
Rethinking Inconsistent Context and Imbalanced Regression in Depression Severity Prediction
Huang, G., Li, J., Lu, H., Guo, M., & Chen, S. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2024.3405584 · Cited by 7
Network structures of symptoms from the Zung Depression ScalePreprint
Briganti, G., Scutari, M., & Linkowski, P. (2020) · doi:10.31234/osf.io/dpwb2 · Cited by 6
Ebrahimi, O. V., Borsboom, D., Hoekstra, R. H. A., Epskamp, S., Ostinelli, E. G., Bastiaansen, J. A., & Cipriani, A. (2023) · doi:10.31234/osf.io/wh6cf · Cited by 6
Bayer, J., van Velzen, L. S., Pozzi, E., Davey, C. G., Han, L. K. M., Bauduin, S., Bauer, J., Benedetti, F., Berger, K., Bonnekoh, L. M., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.17.643677 · Cited by 5
Personalised Deep Learning for Monitoring Depressed Mood from Speech
Gerczuk, M., Triantafyllopoulos, A., Amiriparian, S., Kathan, A., Bauer, J., Berking, M., & Schuller, B. W. (2022) · E-Health and Bioengineering Conference (EHB) · doi:10.1109/ehb55594.2022.9991737 · Cited by 6
Treatment‐resistant depression invites persistent reflection
Parker, G. B. (2023) · World Psychiatry · doi:10.1002/wps.21135 · Cited by 5
Symptom Detection with Text Message Log Distributions for Holistic Depression and Anxiety Screening
Tlachac, M., Heinz, M. V., Reisch, M., & Ogden, S. S. (2024) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3643554 · Cited by 5
The Current Status of the Diagnosis of Depression
Goldberg, D. P. (2015) · History, philosophy and theory of the life sciences · doi:10.1007/978-94-017-7423-9_2 · Cited by 6
Self-citationZulliger R-Optimized in the Evaluation of Depressive Characteristics
Gonçalves, A. P., & de Villemor-Amaral, A. E. (2020) · Paidéia (Ribeirão Preto) · doi:10.1590/1982-4327e3023 · Cited by 5
Karagiannis, T. C., Ververis, K., Liang, J. J., Pitsillou, E., Liu, S., Bresnehan, S. M., Xu, V., Wijoyo, S. J., Duan, X. A., Ng, K., et al. (2024) · Molecules · doi:10.3390/molecules29112446 · Cited by 5
Goldberg, D. P. (2019) · British Journal of General Practice · doi:10.3399/bjgp19x701369 · Cited by 5
Self-citationvan der Wijk, G., Enkhbold, Y., Cnudde, K., Szostakiwskyj, M. W., Blier, P., Knott, V., Jaworska, N., & Protzner, A. B. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1358018 · Cited by 5
Martikkala, A., Baryshnikov, I., Granroth‐Wilding, H. M. V., Heikkilä, R., Riihimäki, K., Saleva, O., Holmén, J., Darst, R. K., Rosenström, T. H., Aledavood, T., et al. (2025) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2025.09.054 · Cited by 4
Nakasujja, N., Vecchio, A., Saylor, D. R., Lofgren, S. M., Nakigozi, G. F., Boulware, D. R., Kisakye, A., Batte, J., Mayanja, R., Anok, A., et al. (2021) · Journal of NeuroVirology · doi:10.1007/s13365-020-00920-6 · Cited by 4
Vilalta, A., Franch, J. V., Serrano-Sarbosa, D., Martí‐Lluch, R., Marrugat, J., & Garre‐Olmo, J. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1192462 · Cited by 4
Dwyer, C. L., Craft, W. H., Tomlinson, D. C., Tegge, A. N., Kim‐Spoon, J., & Bickel, W. K. (2023) · Alcoholism Clinical and Experimental Research · doi:10.1111/acer.15235 · Cited by 4
Parker, G. B., Tavella, G., Spoelma, M. J., & Sazhin, V. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.01.190 · Cited by 5
Un New Deal per le terapie psicodinamiche: lo psicoanalista come burocrate di strada
Clarke, J. C. (2024) · PSICOTERAPIA E SCIENZE UMANE · doi:10.3280/pu2024-002002 · Cited by 4
Biomarkers associated with treatment outcome in young people with depression: A systematic review
Zierotin, A., Mondelli, V., & Zajkowska, Z. (2024) · Neuroscience Applied · doi:10.1016/j.nsa.2024.104082 · Cited by 4
Han, S., Tian, Y. Q., Zheng, R., Wen, B., Liu, L., Liu, H., Wei, Y., Chen, H., Zhao, Z., Xia, M., et al. (2024) · Psychological Medicine · doi:10.1017/s0033291724002617 · Cited by 4
Hongler, K., Lounici, A., Maurer, E., Lanz, U., Szathmári, O., Reuter, Y., Nussbaum, S., Steinborn, I., Haedrich, A., Mölling, M. A., et al. (2024) · NeuroImmunoModulation · doi:10.1159/000542979 · Cited by 6
From dysphoria to anhedonia: age-related shift in the link between cognitive and affective symptoms
Harlev, D., Vituri, A., Shahar, M., & Wolpe, N. (2025) · The Journals of Gerontology Series B · doi:10.1093/geronb/gbaf252 · Cited by 4
Macnamara, C. L., Cvejic, E., Parker, G. B., Lloyd, A. R., Lee, G., Beilharz, J. E., & Vollmer-Conna, U. S. (2018) · Trials · doi:10.1186/s13063-018-2763-8 · Cited by 6
Miron, J., Sheen, J., Panzarella, T., Hyde, M., Mansouri, F., Fox, L., Voetterl, H., Jodoin, V. D., Lespérance, P., Longpré-Poirier, C., et al. (2021) · Journal of Affective Disorders Reports · doi:10.1016/j.jadr.2021.100270 · Cited by 4
Gene expression in peripheral blood in treatment-free major depression
Barboza, A. B. C., Sánchez-Ruiz, J. A., Rodriguez-Sanchez, I. P., González, S., Calvo‐Anguiano, G., Lugo-Trampe, J. D. J., Costilla-Esquivel, A., Martínez, L. E., & Ibarra‐Ramírez, M. (2020) · Acta Neuropsychiatrica · doi:10.1017/neu.2020.5 · Cited by 4
Hlynsson, J. I., Kristjánsson, T., Andersson, G., & Carlbring, P. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1568141 · Cited by 3
Viduani, A. C., Buchweitz, C., Ostermann, A. C., Araujo, R. M., & Kieling, C. (2026) · Psychiatry Research · doi:10.1016/j.psychres.2026.117064 · Cited by 4
Singh, S., Cunningham, J. E. A., Uher, R., Becker, S., & Nunes, A. (2026) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2026.106652 · Cited by 3
Using a longitudinal network structure to subgroup depressive symptoms among adolescents
Liang, S., Huang, Z., Wang, Y., Wu, Y. Z., Chen, Z., Zhang, Y., Guo, W., Zhao, Z., Ford, S. D., Palaniyappan, L. K., et al. (2024) · BMC Psychology · doi:10.1186/s40359-024-01537-8 · Cited by 3
Hierarchical Encoding and Fusion of Brain Functions for Depression Subtype Classification
Liu, M., Zhang, H., Liu, M., Chen, D., Zhou, R., Lu, W., Zhang, L., Shen, D. G., Wang, Q., & Peng, D. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2024.3401251 · Cited by 3
Addressing heterogeneous populations in latent variable settings through robust estimation.
Nieser, K. J., & Cochran, A. L. (2021) · Psychological Methods · doi:10.1037/met0000413 · Cited by 3
Profiles of depressive symptoms in Peru: An 8-year analysis in population-based surveys
Villarreal‐Zegarra, D., Otazú-Alfaro, S., Segovia-Bacilio, P., García-Serna, J., Reátegui-Rivera, C. M., & Meléndez‐Torres, G. J. (2023) · Journal of Affective Disorders · doi:10.1016/j.jad.2023.04.078 · Cited by 5
Zhang, B., Li, Y., Shen, Y., Zhao, W., Yu, Y., & Tang, J. T. (2023) · Psychiatry Research · doi:10.1016/j.psychres.2023.115598 · Cited by 3
Poon, C.-L., & Chen, C.-Y. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.696836 · Cited by 3
Diagnosis Accuracy of Baptista Depression Scale: Adult and Screening Versions
Gonçalves, A. P., Baptista, M. N., de Villemor-Amaral, A. E., & Carvalho, L. D. F. (2021) · Psicologia Teoria e Pesquisa · doi:10.1590/0102.3772e37449 · Cited by 3
Exploring perceptions of people with “modern type depression” among healthcare providers
Kashihara, J., Kameyama, A., Yamakawa, I., Muranaka, M., Matsuura, T., & Sakamoto, S. (2018) · The Japanese journal of psychology · doi:10.4992/jjpsy.89.17334 · Cited by 3
So, H., Lau, A., Chau, C. K., & Wong, S. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/132563 · Cited by 3
Depression symptoms and core affect: Results from network and regression analyses
Vanags, E., & Raščevska, M. (2023) · Mental Health Science · doi:10.1002/mhs2.13 · Cited by 2
Setoyama, D., Kato, T. A., Kunisaki, Y., & Kang, D. (2025) · LabMed discovery. · doi:10.1016/j.lmd.2025.100092 · Cited by 2
Hu, C., Qi, Y., Wang, W., Yan, W., & Peng, K. (2026) · Social Science & Medicine · doi:10.1016/j.socscimed.2026.118938 · Cited by 3
A genome-wide investigation of depression among individuals with and without irritability
St‐Pierre, J., Jang, J., Nagy, C., Fiori, L. M., Turecki, G., Dupuis, J., Bhatnagar, S., & Orri, M. (2026) · Molecular Psychiatry · doi:10.1038/s41380-026-03548-w · Cited by 2
Lu, S., Zhang, A. Y., Liu, T., Leung, D. K. Y., Kwok, W.-W., Luo, H., Tang, J. Y. ‐., Wong, G. H. Y., & Lum, T. Y. S. (2021) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.5636 · Cited by 2
Vike, N. L., Bari, S., Kim, B. W., Katsaggelos, A. K., Blood, A. J., & Breiter, H. C. (2024) · PLoS ONE · doi:10.1371/journal.pone.0299528 · Cited by 2
Su, Y., Li, M., Schmitz, N., & Meng, X. (2024) · Psychiatry and Clinical Neurosciences · doi:10.1111/pcn.13728 · Cited by 2
The social–ecological model of depressive symptoms in middle aged in China: a network analysis
Shen, G., Chen, Y.-H., Zou, Y., Wu, S., Zheng, Y., & Chen, L. (2025) · BMC Public Health · doi:10.1186/s12889-025-22699-z · Cited by 2
Chen, B. K., Whye, A., Matthews, L. C., Moniz, T., Mendez‐David, I., Gardier, A. M., David, D. J. P., Johns, S., Weisblum, E., & Denny, C. A. (2025) · Neuropsychopharmacology · doi:10.1038/s41386-025-02107-1 · Cited by 3
The economic burden of treatment-resistant depression: Cost-of-illness perspective
Niazi, S. K., & Bobo, W. V. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-824067-0.00003-7 · Cited by 2
Redesigning antidepressant drug discovery
Holsboer, F. (2014) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2014.16.1/fholsboer · Cited by 2
Wong, B. C., Chau, C. K., Ao, F., Mo, C., Wong, S., Wong, Y. H., & So, H. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/140590 · Cited by 2
Commentary: Recent disputes on the role of serotonin in depression
Rose, N. (2023) · Psychological Medicine · doi:10.1017/s0033291723002726 · Cited by 2
Heart rate variability during inpatient treatment of depression
Geiss, L., Beck, B., Stemmler, M., Hillemacher, T., & Hösl, K. (2024) · Journal of mood and anxiety disorders · doi:10.1016/j.xjmad.2024.100059 · Cited by 1
“It Seems to Sort of Wash My Brain Out”: The Experiences of Open-Water Swimmers with Depression
Crook, I., Brown, R. J., & Bell, T. (2025) · OBM Integrative and Complementary Medicine · doi:10.21926/obm.icm.2502022 · Cited by 1
Huang, C. H. O., Lau, H. L. N., Yuan, G. F., Liu, C., Lam, S. K. K., & Fung, H. W. (2025) · Healthcare · doi:10.3390/healthcare13172163 · Cited by 1
Coutts, F., Mena, S., Ucur, E., Fleischhacker, W. W., Kahn, R. S., Lieberman, J. A., Hasan, A., Howes, O. D., Correll, C. U., Koutsouleris, N., et al. (2025) · Psychiatry and Clinical Neurosciences · doi:10.1111/pcn.13914 · Cited by 1
Peripheral immune system activity in young psychiatry patients
Seizer, L., Löchner, J., & Renner, T. J. (2025) · Brain Behavior & Immunity - Health · doi:10.1016/j.bbih.2025.101143 · Cited by 1
Kang, J., Kong, N., Kim, S., & Lee, H. (2026) · BMC Psychiatry · doi:10.1186/s12888-026-07893-6 · Cited by 1
Differential effects of transcranial direct current stimulation on depression symptom clusters
Razza, L. B., Goerigk, S. A., Nikolin, S., Díaz, A. P., Moffa, A. H., Nord, C. L., Valiengo, L. D. C. L., Borrione, L., Loo, C. K. A., Myung, W., et al. (2026) · Journal of Affective Disorders · doi:10.1016/j.jad.2026.121421 · Cited by 1
Liang, S., Tan, Z.-L., Ding, J., Dai, Y., Xu, Y., Ma, J., Song, X. M., Yeo, B. T. T., & Li, T. (2026) · Molecular Psychiatry · doi:10.1038/s41380-026-03524-4 · Cited by 1
Fung, H. W., Li, Y., Fong, C. K. M., Wang, S.-C., Huang, S.-Y., Reyes, M. E. S., Bengwasan, P. D., Lian, A. E. Z., Wong, M. Y. C., Low, S. R., et al. (2026) · Journal of Affective Disorders · doi:10.1016/j.jad.2026.121698 · Cited by 1
Kim, Y. E., Lim, H. R., & Lee, T. (2026) · Investigative Magnetic Resonance Imaging · doi:10.13104/imri.2025.0049 · Cited by 1
Veniaminova, E. A., & Strekalova, T. V. (2016) · Neurochemical Journal · doi:10.1134/s1819712416010153 · Cited by 1
Is MDD the right target for early-stage psychedelic-assisted therapy trials?
Lewis, B. R., & Byrne, K. (2021) · Journal of Psychedelic Studies · doi:10.1556/2054.2021.00180 · Cited by 1
Wang, L., Chignell, M., Jiang, H., Lokuge, S., Mason, G. E., Fotinos, K., & Katzman, M. A. (2021) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm52615.2021.9669356 · Cited by 1
Wong, W. L. E., Arathimos, R., Lewis, C. M., Young, A. H., & Dawe, G. S. (2023) · PLoS ONE · doi:10.1371/journal.pone.0294045 · Cited by 1
Work Impairment in Patients with Depression: A Descriptive Cross Section Study
Elmetwaly, M. M. F., El‐Gilany, A., Elwasify, M., & Elkalla, I. (2024) · Benha Medical Journal · doi:10.21608/bmfj.2024.141296.1600 · Cited by 1
Using Random Effects Machine Learning Algorithms to Identify Vulnerability to Depression
Bhaumik, R., & Stange, J. P. (2023) · PubMed · doi:10.25417/uic.24699042 · Cited by 1
Mental health phenotypes of well-controlled HIV in Uganda
Rubin, L. H., Cho, K., Bolzenius, J. D., Mannarino, J. A., Easter, R. E., Dastgheyb, R. M., Anok, A., Tomusange, S., Saylor, D. R., Wawer, M. J., et al. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1407413 · Cited by 1
The role of rare copy number variants in early‐onset depression
Dennison, C. A., Sønderby, I. E., Garcia‐Argibay, M., Powell, V. E., Shakeshaft, A., Riglin, L., Rees, E., O’Donovan, M. C., Larsson, H., Andreassen, O. A., et al. (2026) · JCPP Advances · doi:10.1002/jcv2.70128 · Cited by 1
A Phenomenological Reappraisal of Dynamical Systems in Psychopathology
Kyzar, E. J., Denfield, G. H., Feyaerts, J., Sass, L. A., & Nelson, B. (2025) · Psychopathology · doi:10.1159/000548025 · Cited by 2
Precision Medicine and a Learning Health System for Mental Health
Ranallo, P. A., & Tenenbaum, J. D. (2021) · Health informatics · doi:10.1007/978-3-030-70558-9_1 · Cited by 1
Kraft, B., Bø, R., Hoorelbeke, K., Koster, E. H. W., Jonassen, R., Harmer, C. J., & Landrø, N. I. (2023) · Psychiatry Research Communications · doi:10.1016/j.psycom.2023.100137 · Cited by 1
Kasthurirathne, S. N. (2018) · IUScholarWorks (Indiana University) · doi:10.7912/c2/910 · Cited by 1
van der Wijk, G., Enkhbold, Y., Cnudde, K., Szostakiwskyj, M. W., Blier, P., Knott, V., Jaworska, N., & Protzner, A. B. (2020) · medRxiv · doi:10.1101/2020.11.09.20227280 · Cited by 1
Sleep spindles and cognition in depressionPreprint
Bovy, L., Dresler, M., & Weber, F. D. (2017) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/waz3d · Cited by 1
Meyyappan, A. C., Forth, E., & Milev, R. V. (2021) · Research Square · doi:10.21203/rs.3.rs-374466/v1 · Cited by 1
From Research to Practice: Clinical Utility of Quantitative NosologyPreprint
Lin, S.-Y., & Eaton, N. R. (2020) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/fk9xq · Cited by 1
PHQ-9, Life Impact, and Breadth of Symptomatic ExperiencePreprint
Newson, J. J., Parameshwaran, D., & Thiagarajan, T. C. (2023) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/p7q8x · Cited by 1
Unravelling the Role of the Dopamine D1 Receptor in Anhedonia, Asociality, and Avolition
Witt, K. (2023) · doi:10.26686/wgtn.24646128 · Cited by 1
Bergmann, E., Harlev, D., Cam-CAN, & Wolpe, N. (2023) · medRxiv · doi:10.1101/2023.11.26.23299020 · Cited by 1
Ángeles-Valdéz, D., van Tol, M., & Garza‐Villarreal, E. A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.12.648523 · Cited by 1
Misner, S. J. (2013) · ProQuest Demo Repository
Psychiatric symptoms and disorders in old age: prevalence, course and diagnostic thresholds
Sigström, R. (2015) · Gothenburg University Publications Electronic Archive (Gothenburg University)
The Folly of Systems: The Satiric Tradition and Mental Disorders
Justman, S. (2015) · Palgrave Macmillan US eBooks · doi:10.1057/9781137523297_7
Phillips, J. L. (2015) · uO Research (University of Ottawa) · doi:10.20381/ruor-2800
Hunter, N. (2018) · Springer eBooks · doi:10.1007/978-3-319-91752-8_8
Urban mental health and the moral economies of sufferingin a ‘broken city’
Wenceslau, L. D., & Ortega, F. (2020) · Manchester University Press eBooks · doi:10.7765/9781526150943.00012
Giacoletti, G., Msdi, A. S., Cook, R., & Brust, T. F. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-817935-2.00044-1 · Cited by 1
Clark, E. L. (2021) · cIRcle (University of British Columbia) · doi:10.14288/1.0400894
Constructing and applying explanatory models in clinical psychology
Pfeiff, J. S. (2021) · Institutional Repository of Leibniz Universität Hannover (Leibniz Universität Hannover) · doi:10.15488/11363
Precision Medicine: Neuropsychodynamic Psychiatry Model of Major Depressive Disorder
Yaqi, H. (2021) · Advances in Social Science, Education and Humanities Research/Advances in social science, education and humanities research · doi:10.2991/assehr.k.211220.268
Usher, C. T. (2022) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2022.04.017
Goldberg, D. P. (2015) · Social Psychiatry and Psychiatric Epidemiology · doi:10.1007/s00127-015-1034-1
Self-citation
Metadata
- Authors
- GOLDBERG, D.
- Affiliations
- King's College London (GB)
- Corresponding author
- GOLDBERG, DAVID
- ORCID
- GOLDBERG, DAVID
- Venue
- World Psychiatry
- Publisher
- Wiley
- Published
- 2011-10
- Volume
- 10
- Issue
- 3
- Pages
- 226-228
- ISSN
- 1723-8617 (ISSN-L)
- 2051-5545
- PMC
- PMC3188778
- OpenAlex
- W1524375783
- Cited by
- 335
- References
- 15
- Citations by year
- 2026: 26 · 2025: 34 · 2024: 40 · 2023: 42 · 2022: 30 · 2021: 49 · 2020: 24 · 2019: 17 · 2018: 17 · 2017: 8 · 2016: 12 · 2015: 20 · 2014: 6 · 2013: 7 · 2012: 3
- FWCI
- 6.59
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- World Psychiatry (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Health Sciences › Medicine › Pharmacology (medical) › Treatment of Major Depression
- Keywords
- major depressive disorder, depression heterogeneity, depressive symptom clusters, DSM diagnostic criteria, bipolar depression, atypical depression, endogenous depression, agitated depression, antidepressant selection, depression subtypes, drug-induced depression
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review