Method matters: Understanding diagnostic reliability in DSM-IV and DSM-5.
Bibliographic record
Abstract
Diagnostic reliability is essential for the science and practice of psychology, in part because reliability is necessary for validity. Recently, the DSM-5 field trials documented lower diagnostic reliability than past field trials and the general research literature, resulting in substantial criticism of the DSM-5 diagnostic criteria. Rather than indicating specific problems with DSM-5, however, the field trials may have revealed long-standing diagnostic issues that have been hidden due to a reliance on audio/video recordings for estimating reliability. We estimated the reliability of DSM-IV diagnoses using both the standard audio-recording method and the test-retest method used in the DSM-5 field trials, in which different clinicians conduct separate interviews. Psychiatric patients (N = 339) were diagnosed using the SCID-I/P; 218 were diagnosed a second time by an independent interviewer. Diagnostic reliability using the audio-recording method (N = 49) was "good" to "excellent" (M κ = .80) and comparable to the DSM-IV field trials estimates. Reliability using the test-retest method (N = 218) was "poor" to "fair" (M κ = .47) and similar to DSM-5 field-trials' estimates. Despite low test-retest diagnostic reliability, self-reported symptoms were highly stable. Moreover, there was no association between change in self-report and change in diagnostic status. These results demonstrate the influence of method on estimates of diagnostic reliability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.405 | 0.595 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".