Estimating remission from untreated major depression: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Few studies have examined spontaneous remission from major depression. This study investigated the proportion of prevalent cases of untreated major depression that will remit without treatment in a year, and whether remission rates vary by disorder severity. METHOD: Wait-list controlled trials and observational cohort studies published up to 2010 with data describing remission from untreated depression at ≤ 2-year follow-up were identified. Remission was defined as rescinded diagnoses or below threshold scores on standardized symptom measures. Nineteen studies were included in a regression model predicting the probability of 12-month remission from untreated depression, using logit transformed remission proportion as the dependent variable. Covariates included age, gender, study type and diagnostic measure. RESULTS: Wait-listed compared to primary-care samples, studies with longer follow-up duration and older adult compared to adult samples were associated with lower probability of remission. Child and adolescent samples were associated with higher probability of remission. Based on adult samples recruited from primary-care settings, the model estimated that 23% of prevalent cases of untreated depression will remit within 3 months, 32% within 6 months and 53% within 12 months. CONCLUSIONS: It is undesirable to expect 100% treatment coverage for depression, given many will remit before access to services is feasible. Data were drawn from consenting wait-list and primary-care samples, which potentially over-represented mild-to-moderate cases of depression. Considering reported rates of spontaneous remission, a short untreated period seems defensible for this subpopulation, where judged appropriate by the clinician. Conclusions may not apply to individuals with more severe depression.
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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 | 0.031 | 0.071 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.040 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".