Problematic features of episode-based definitions of depression and a preliminary proposal for their replacement
Bibliographic record
Abstract
Episodes of depression are constructed by imposing temporal and symptom-severity thresholds onto symptom levels that vary over time, resulting in a loss of information. Consequently, it is widely acknowledged that binary categorization of depressive episodes is problematic. Binary classification can make similar symptom levels appear different and different symptom levels appear similar. Furthermore, symptom severity is only one of several thresholds that are applied in the construction of depressive episodes in DSM-5 and ICD-11, others being: a minimum duration of symptoms, the application of a "no significant symptoms" threshold for remission, and time requirements (e.g., 2 months) for remission. Application of each of these thresholds leads to a loss of information. The joint occurrence of these four thresholds creates a complex set of circumstances in which similar patterns of symptoms may be categorized differently and different patterns may be categorized as similar. The ICD-11 definition can be expected to lead to better classification than the DSM-5 approach since it does not require two symptom-free months for remission, eliminating one of four problematic thresholds. A more radical change would be to adopt a truly dimensional perspective which would need to incorporate new elements to reflect time spent at various levels of depression. Such an approach, however, seems feasible both in clinical practice and research.
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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 | 0.057 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.010 | 0.024 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".