Assessing full remission
Bibliographic record
Abstract
The 17-item Hamilton Rating Scale for Depression (HAM-D17) has been used for 4 decades as the "gold standard" instrument to assess the severity of depression and response to therapy in clinical research. The clinical utility of the HAM-D17 is hampered, in part, by the length of time required to administer the interview and by concern about a lack of inter-rater reliability. Several groups have developed shorter versions of the HAM-D17 for use in clinical practice. However, despite extensive research highlighting the importance of achieving full remission in minimizing the risk of relapse and recurrence, these shortened questionnaires have not been validated for the task of distinguishing between remission and response. A shortened form of the HAM-D17 with cut-off scores for full remission would offer a useful tool that physicians could readily employ in clinical practice. On the basis of the responses of a sample of 292 patients with major depression who received standard clinical treatment at a tertiary university affiliated hospital (Depression Clinic, Centre for Addiction and Mental Health, Toronto Ont.) we derived a shortened version of the HAM-D. Seven items with the greatest frequency of occurrence and sensitivity to change with treatment were identified and designated as the Toronto HAM-D7. A score of 3 or less on the Toronto HAM-D7 was found to correlate with the 17-item HAM-D definition of full remission (i.e., score of 7 or less).
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".