Support, Patience, and Expectancy: Therapeutic Options Alongside Intensified Treatments for Depression?
Bibliographic record
Abstract
In addition to a scoping review by Dr Rachelle Ashcroft and colleagues1 describing incentives and disincentives for depression treatment and a sex-specific analysis of depressive symptoms in members of the Canadian Armed Forces by Dr Jitender Sareen and colleagues (see Erickson et al2), this issue of The Canadian Journal of Psychiatry includes 2 Original Research papers on treatment-resistant depression (TRD).3,4 For the first of these, Ms Sakina Rizvi and colleagues3 collected data in primary care settings in Canada. An estimated 21.7 % of depressed patients in primary care practices were found to have TRD. The authors point out that their estimate is slightly lower than one arising from the Sequenced Treatment Alternatives to Relieve Depression (commonly referred to as the STAR*D) trial. However, both estimates are very high when compared with community populations. In epidemiologic studies, many new-onset episodes are brief, lasting only a few weeks, even though they are often untreated. The TRD episodes described by Ms Rizvi and colleagues had a mean duration of 36 months (12 months in non-TRD episodes), and they report that more than one-half of these patients were taking multiple psychotropics. Epidemiologically, this is not surprising. Roughly speaking, prevalence is equal to the
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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 | 0.007 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.020 | 0.022 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".