Non-remission of depression in the general population as assessed by the HAMD-7 scale
Bibliographic record
Abstract
Remission from the symptoms of depression is the optimal outcome for depression treatment. Many studies have assessed the frequency of treatment, but there are none that have estimated the frequency of treated remission in the general population. We addressed this issue in the population of Alberta using a brief Hamilton Depression Rating Scale (HAMD)-7 scale (recently validated against the HAMD-17 scale in a clinical setting) that has been proposed as a suitable indicator for remission in primary care. We used data from a survey conducted within the Alberta Depression Initiative in 2005 (n=3,345 adults), to produce a population-based estimate of the number of respondents taking antidepressant medication for depression. From this group we selected a subpopulation that did not screen positive when the MINI module for major depression was administered (i.e., who did not have an active episode). Non-remission in this subpopulation was assessed with a version of the HAMD-7 scale adapted for telephone administration by a nonclinician. Of the survey respondents, 189 reported taking antidepressant medication for depression. Of these, 115 were found not to have an active episode. However, 49.0% of this subpopulation was not in remission as evaluated by the HAMD-7. We estimate that 1.3% (95% confidence interval, 0.9-2.0%) of the population is in treated non-remission for depression. Our study indicates a substantial degree of non-remission from depression in individuals taking antidepressants in the general population. This suggests that, in addition to increasing the frequency of treatment, increasing the effectiveness of treatment can have an impact on population health.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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; 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".