Basing psychiatric classification on scientific foundation: Problems and prospects
Bibliographic record
Abstract
To examine whether and how the classification of mental disorders can be based on research, we evaluate the relevance of psychiatric science to the major questions in classification. We conclude that most studies cannot inform the validity of diagnostic categories because they are constrained by the classification through a top-down diagnostic approach. Analyses of relationships between diagnostic categories suggest that most interdiagnostic boundaries in current classifications lack validity. Likewise, genetic studies show that the susceptibility to mental illness is at most partly disorder-specific. Neuroimaging research is uninformative due to unsystematic single-diagnosis studies, use of super-healthy controls, and publication bias. Treatment research suggests moderate specificity in several areas of psychopathology (e.g. lithium for bipolar disorder), but lack of specificity is the rule (e.g. the broad indications of serotonin-reuptake inhibitors). In summary, evidence from multiple lines of research converges to indicate that current classifications contain excessively large numbers of categories of limited validity. Dimensional classification will not solve the problem because the number of dimensions is as uncertain as the number of categories. Psychiatric research should discard the assumption that current classification is valid. Instead of diagnosis-specific investigations, studies of unselected groups assessed with bottom-up approaches are needed to advance psychiatry.
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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 | 0.333 | 0.397 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.007 | 0.066 |
| Scholarly communication | 0.018 | 0.051 |
| Open science | 0.012 | 0.015 |
| Research integrity | 0.018 | 0.019 |
| Insufficient payload (model declined to judge) | 0.007 | 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".