How Do Clinicians Actually Use the Diagnostic and Statistical Manual of Mental Disorders in Clinical Practice and Why We Need to Know More
Bibliographic record
Abstract
The clinical use of the Diagnostic and Statistical Manual of Mental Disorders (DSM) is explicitly stated as a goal for both the DSM Fourth Edition and DSM Fifth Edition (DSM-5) revisions. Many uses assume a relatively faithful application of the DSM diagnostic definitions. However, studies demonstrate significant discrepancies between clinical psychiatric diagnoses with those made using structured interviews suggesting that clinicians do not systematically apply the diagnostic criteria. The limited information regarding how clinicians actually use the DSM raises important questions: a) How can the clinical use be improved without first having a baseline assessment? b) How can potentially significant shifts in practice patterns based on wording changes be assessed without knowing the extent to which the criteria are used as written? Given the American Psychiatric Association's plans for interim revisions to the DSM-5, the value of a detailed exploration of its actual use in clinical practice remains a significant ongoing concern and deserves further study including a number of survey and in vivo studies.
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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 | 0.042 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".