CoChat
Sign Up
Cited by 5

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Latent profile analysis of blood marker phenotypes and their relationships with clinical pain and interference reports in people with acute musculoskeletal trauma

Lee, Joshua Y., Walton, David M.·2021·Canadian Journal of Pain
DOI 10.1080/24740527.2020.1870102PMC PMC7951147
Open this paper in CoChat — free account

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1080/24740527.2020.1870102

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1. Graphical representation of the three-class latent profile solution along with the frequencies of each class. All eight target markers presented in a three-class profile solution were labeled accordingly: class 1 = low biomarker concentration (32.8% of the sample), class 2 = average biomarker concentration (49.0%), class 3 = high BDNF and TGF-β1 (18.2%). Relative concentration represents z-transformed values
Figure 1. Graphical representation of the three-class latent profile solution along with the frequencies of each class. All eight target markers presented in a three-class profile solution were labeled accordingly: class 1 = low biomarker concentration (32.8% of the sample), class 2 = average biomarker concentration (49.0%), class 3 = high BDNF and TGF-β1 (18.2%). Relative concentration represents z-transformed valuesCC BY · PubMed Central
Figure 2. Graphical representation of the three-class latent profile solution adequately described by three of the eight markers. Classes were labeled accordingly: class 1 = low biomarker concentration (33.9% of the sample), class 2 = average biomarker concentration (47.7%), and class 3 = high BDNF and TGF-β1 (18.3%). Relative concentration represents z-transformed values
Figure 2. Graphical representation of the three-class latent profile solution adequately described by three of the eight markers. Classes were labeled accordingly: class 1 = low biomarker concentration (33.9% of the sample), class 2 = average biomarker concentration (47.7%), and class 3 = high BDNF and TGF-β1 (18.3%). Relative concentration represents z-transformed valuesCC BY · PubMed Central

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.

A few free checks a day · papers already verified open instantly · results are public

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?

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

Show all 3 references

Research works printed but not linked: 63

  1. Miller ET, 2019, Pain Manag Nurs, 20, 91, 10.1016/j.pmn.2019.02.001 · DOI
  2. 10.3390/healthcare3030838 · DOI
  3. Berube M, 2016, Int J Orthop Trauma Nurs, 23, 47, 10.1016/j.ijotn.2016.04.002 · DOI
  4. 10.1016/j.ijotn.2016.04.001 · DOI
  5. 10.1097/j.pain.0000000000000580 · DOI
  6. 10.1097/j.pain.0000000000001065 · DOI
  7. Dunn KM, 2013, BMJ Open, 3, e003838, 10.1136/bmjopen-2013-003838 · DOI
  8. Dunn KM, 2006, Am J Epidemiol, 163, 754, 10.1093/aje/kwj100 · DOI
  9. Carey TS, 1999, Med Care, 37, 157, 10.1097/00005650-199902000-00006 · DOI
  10. 10.1016/j.pain.2011.01.056 · DOI
  11. Lee JY, 2020, BMC Musculoskelet Disord, 21, 615, 10.1186/s12891-020-03621-7 · DOI
  12. 10.1111/nyas.13551 · DOI
  13. Li Y, 2016, Biosci Rep, 36
  14. 10.1016/j.bbi.2016.10.003 · DOI
  15. Cleeland CS, 1991, Pain Res Group
  16. Cleeland C, 1994, Pain assessment: global use of the brief pain inventory
  17. 10.1097/00002508-200409000-00005 · DOI · Paper page
  18. 10.1371/journal.pone.0069460 · DOI
  19. 10.1007/s12160-011-9297-6 · DOI
  20. Sacerdote P, 2013, J Neuroimmune Pharmacol, 8, 202, 10.1007/s11481-012-9428-2 · DOI
  21. 10.1016/j.cell.2009.09.028 · DOI · Paper page
  22. 10.1016/j.neuint.2003.09.009 · DOI
  23. 10.3109/08977199309029129 · DOI
  24. 10.1093/brain/awy009 · DOI
  25. Bio-Plex Manager Software [computer program], Version 4.1.1. Hercules, CA: Bio-Rad Laboratories, Inc
  26. 10.1177/0013164405284033 · DOI
  27. MPLUS [computer program], Version 6.12. Los Angeles, CA
  28. 10.1093/aje/kwt245 · DOI
  29. 10.1111/j.1751-9004.2007.00054.x · DOI
  30. 10.1007/BF01246098 · DOI · Paper page
  31. 10.1080/10705510701575396 · DOI · Paper page
  32. 10.1177/0165025409343765 · DOI · Paper page
  33. 10.2307/1912557 · DOI · Paper page
  34. 10.5395/rde.2013.38.1.52 · DOI · Paper page
  35. 10.1080/01973533.2016.1277529 · DOI · Paper page
  36. 10.5114/aoms.2015.56342 · DOI · Paper page
  37. 10.1126/science.286.5443.1358 · DOI
  38. 10.1016/S0092-8674(00)80595-4 · DOI · Paper page
  39. 10.1056/NEJM200005043421807 · DOI
  40. McKay SE, 1999, Learn Mem, 6, 193, 10.1101/lm.6.3.193 · DOI
  41. Bailey CH, 1991, Ann N Y Acad Sci, 627, 181, 10.1111/j.1749-6632.1991.tb25924.x · DOI
  42. 10.1126/sciadv.aav9946 · DOI
  43. 10.1007/s12035-011-8221-1 · DOI
  44. 10.1002/jnr.1229 · DOI
  45. 10.1093/cercor/bhx095 · DOI
  46. 10.1046/j.1460-9568.2003.02886.x · DOI
  47. Jones ME, 2018, Brain Behav Immun, 67, 355, 10.1016/j.bbi.2017.09.016 · DOI
  48. 10.1038/ni1276 · DOI · Paper page
  49. 10.3389/fimmu.2016.00160 · DOI · Paper page
  50. 10.1159/000087655 · DOI · Paper page
  51. Austin PJ, 2019, Curr Opinion Physiol, 11, 7, 10.1016/j.cophys.2019.03.008 · DOI
  52. 10.1016/j.bbi.2018.09.010 · DOI
  53. 10.1371/journal.pone.0077903 · DOI
  54. 10.1016/j.psyneuen.2004.04.007 · DOI
  55. Izawa S, 2013, Brain Behav Immun, 27, 38, 10.1016/j.bbi.2012.07.001 · DOI
  56. 10.1007/s00005-015-0357-7 · DOI
  57. 10.1186/1479-5876-9-113 · DOI
  58. 10.1007/BF02989591 · DOI
  59. 10.2165/11534530-000000000-00000 · DOI · Paper page
  60. 10.1152/ajpendo.00074.2003 · DOI · Paper page
  61. Welc SS, 2013, Exp Physiol, 98, 359, 10.1113/expphysiol.2012.068189 · DOI
  62. 10.3892/mmr.2013.1263 · DOI
  63. Meeus M, 2018, Pain Physician, 21, 541, 10.36076/ppj.2018.6.541 · DOI

Cited by

Sorted by citations

Metadata

Authors
Lee, J. Y., & Walton, D. M.
Affiliations
Faculty of Health Sciences, Western University, London, Ontario, Canada
ORCID
Walton, David M.
Venue
Canadian Journal of Pain
Publisher
Informa UK Limited
Published
2021-01-01
Volume
5
Issue
1
Pages
30-42
ISSN
2474-0527
DOI
10.1080/24740527.2020.1870102
PMC
PMC7951147
Cited by
5
References
3
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (unspecified)
Funders
Canadian Pain Society

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