The human cost of not achieving full remission in depression.
Bibliographic record
Abstract
Depression is among the most disabling and costly illnesses in the world. Despite good short-term efficacy outcomes in the treatment of depression, long-term outcomes remain disappointing. Depression continues to be missed or underdiagnosed and undertreated, and comorbidities are frequently not identified. Of particular concern is the low rate of depression treated to full remission. Treating only to response leaves patients with residual depressive symptoms and an increased risk of a recurrent or chronic course. Anything less than full remission should be considered a treatment failure. This article examines the substantial psychiatric, medical, functional, and economic costs associated with not achieving remission. Available pharmacoeconomic data and randomized, controlled clinical trials published in the last 5 years identified through Medline searches with terms including burden, cost, economics, serotonin reuptake inhibitors (also, specific agents), venlafaxine, nefazadone, mirtazapine, psychotherapy, remission, and depression were reviewed. One of the limiting factors to this review is that few trials have compared the effects of various antidepressant strategies on clinically relevant outcomes such as depression-free days and patient productivity, making the full benefit of remission more difficult to measure. Patients who fail to achieve a full remission have a more recurrent and chronic course, increased medical and psychiatric comorbidities, greater functional burden, and increased social and economic costs. Cost-effective treatment for depression includes antidepressant therapies with higher remission rates. Antidepressants with a dual mechanism of action and combination therapies are associated with higher remission rates, more depression-free days, reduced pain-symptom morbidity, reduced health service utilization, and improved productivity.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".