The DOI matches its Crossref record; 125 of the 125 references it cites were found in a registry or an index.
Development and validation of methods for assessing the quality of diagnostic accuracy studies
Open accessAbstract
OBJECTIVES: To develop a quality assessment tool which will be used in systematic reviews to assess the quality of primary studies of diagnostic accuracy. DATA SOURCES: Electronic databases including MEDLINE, EMBASE, BIOSIS and the methodological databases of both CRD and the Cochrane Collaboration. REVIEW METHODS: Three systematic reviews were conducted to provide an evidence base for the development of the quality assessment tool. A Delphi procedure was used to develop the quality assessment tool and the information provided by the reviews was incorporated into this. A panel of nine experts in the area of diagnostic accuracy studies took part in the Delphi procedure to agree on the items to be included in the tool. Panel members were also asked to provide feedback on various other items and whether they would like to see the development of additional topic and design specific items. The Delphi procedure produced the quality assessment tool, named the QUADAS tool, which consisted of 14 items. A background document was produced describing each item included in the tool and how each of the items should be scored. RESULTS: The reviews produced 28 possible items for inclusion in the quality assessment tool. It was found that the sources of bias supported by the most empirical evidence were variation by clinical and demographic subgroups, disease prevalence/severity, partial verification bias, clinical review bias and observer/instrument variation. There was also some evidence of bias for the effects of distorted selection of participants, absent or inappropriate reference standard, differential verification bias and review bias. The evidence for the effects of other sources of bias was insufficient to draw conclusions. The third review found that only one item, the avoidance of review bias, was included in more than 75% of tools. Spectrum composition, population recruitment, absent or inappropriate reference standard and verification bias were each included in 50-75% of tools. Other items were included in less than 50% of tools. The second review found that the quality assessment tool should have the potential to be discussed narratively, reported in a tabular summary, used as recommendations for future research, used to conduct sensitivity or regression analyses and used as criteria for inclusion in the review or a primary analysis. This suggested that some distinction is needed between high- and low-quality studies. Component analysis was considered the best approach to incorporate quality into systematic reviews of diagnostic studies and this was taken into consideration when developing the tool. CONCLUSIONS: This project produced an evidence-based quality assessment tool to be used in systematic reviews of diagnostic accuracy studies. Through the various stages of the project the current lack of such a tool and the need for a systematically developed validated tool were demonstrated. Further work to validate the tool continues beyond the scope of this project. The further development of the tool by the addition of design- and topic-specific criteria is proposed.
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.
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
Intraclass correlations: Uses in assessing rater reliability.
Shrout, P. E., & Fleiss, J. L. (1979) · Psychological Bulletin · doi:10.1037/0033-2909.86.2.420 · Cited by 24,532
Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine
Zweig, M. H., & Campbell, G. (1993) · Clinical Chemistry · doi:10.1093/clinchem/39.4.561 · Cited by 6,569
Schulz (1995) · doi:10.1001/jama.1995.03520290060030 · Cited by 5,634
Two-sided confidence intervals for the single proportion: comparison of seven methods
Newcombe, R. G. (1998) · Statistics in Medicine · doi:10.1002/(sici)1097-0258(19980430)17:8<857::aid-sim777>3.0.co;2-e · Cited by 5,107
Moher, D., Pham, B., Jones, A., Cook, D. J., Jadad, A. R., Moher, M., Tugwell, P., & Klassen, T. P. (1998) · The Lancet · doi:10.1016/s0140-6736(98)01085-x · Cited by 3,560
Empirical evidence of design-related bias in studies of diagnostic tests.
Lijmer, J. G. (1999) · JAMA · doi:10.1001/jama.282.11.1061 · Cited by 1,861
Problems of Spectrum and Bias in Evaluating the Efficacy of Diagnostic Tests
Ransohoff, D. F., & Feinstein, A. R. (1978) · New England Journal of Medicine · doi:10.1056/nejm197810262991705 · Cited by 1,734
Systematic reviews of evaluations of diagnostic and screening tests
Deeks, J. J. (2001) · BMJ · doi:10.1136/bmj.323.7305.157 · Cited by 1,340
The STARD Statement for Reporting Studies of Diagnostic Accuracy: Explanation and Elaboration
Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L. M., Moher, D., Rennie, D., de Vet, H. C. W., & Lijmer, J. G. (2003) · Annals of Internal Medicine · doi:10.7326/0003-4819-138-1-200301070-00012-w1 · Cited by 1,124
Self-citation
Cited by
Whiting, P., Rutjes, A. W., Reitsma, J. B., Bossuyt, P. M., & Kleijnen, J. (2003) · BMC Medical Research Methodology · doi:10.1186/1471-2288-3-25 · Cited by 3,592
Self-citationOlsson, B., Lautner, R., Andreasson, U., Öhrfelt, A., Portelius, E., Bjerke, M., Hölttä, M., Rosén, C., Olsson, C., Strobel, G., et al. (2016) · The Lancet Neurology · doi:10.1016/s1474-4422(16)00070-3 · Cited by 2,049
Zeng, X., Zhang, Y., Kwong, J. S. W., Zhang, C., Li, S., Sun, F., Niu, Y., & Du, L. (2015) · Journal of Evidence-Based Medicine · doi:10.1111/jebm.12141 · Cited by 2,082
Ma, L.-L., Wang, Y.-Y., Yang, Z.-H., Huang, D., Weng, H., & Zeng, X.-T. (2020) · Military Medical Research · doi:10.1186/s40779-020-00238-8 · Cited by 1,648
Gilbody, S., Richards, D., Brealey, S., & Hewitt, C. (2007) · Journal of General Internal Medicine · doi:10.1007/s11606-007-0333-y · Cited by 1,290
Metadata
- Authors
- Whiting, P., Rutjes, A., Dinnes, J., Reitsma, J., Bossuyt, P., & Kleijnen, J.
- Venue
- Health Technology Assessment
- Published
- 2004-06
- Volume
- 8
- Issue
- 25
- Pages
- iii, 1-234
- ISSN
- 1366-5278 (ISSN-L)
- 2046-4924
- OpenAlex
- W2004084637
- Cited by
- 372
- References
- 125
- FWCI
- 24.03
- Type
- journal article
- Language
- en
- Open access
- diamond
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Development and validation of methods for assessing the quality of diagnostic accuracy studies. https://app.cochat.ai/papers/10.3310/hta8250
- The paper
- Whiting, P., Rutjes, A., Dinnes, J., Reitsma, J., Bossuyt, P., & Kleijnen, J. (2004). Development and validation of methods for assessing the quality of diagnostic accuracy studies. Health Technology Assessment. https://doi.org/10.3310/hta8250
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.