Bibliographic record
Abstract
BACKGROUND: Epidemiological data have shown that the probability of recovery from an episode declines with increasing episode duration, such that the duration of an episode may be an important factor in determining whether treatment is required. The objective of this study is to incorporate episode duration data into a calculator predicting the probability of recovery during a specified interval of time. METHODS: Data from two Canadian epidemiological studies were used, both studies were components of a program undertaken by the Canadian national statistical agency. One component was a cross-sectional psychiatric epidemiological survey (n = 36,984) and the other was a longitudinal study (n = 17,262). RESULTS: A Weibull distribution provided a good description of episode durations reported by subjects with major depression in the cross-sectional survey. This distribution was used to develop a discrete event simulation model for episode duration calibrated using the longitudinal data. The resulting estimates were then incorporated into a predictive calculator. During the early weeks of an episode, recovery probabilities are high. The model predicts that approximately 20% will recover in the first week after diagnostic criteria for major depression are met. However, after six months of illness, recovery during a subsequent week is less than 1%. CONCLUSION: The duration of an episode is relevant to the probability of recovery. This epidemiological feature of depressive disorders can inform prognostic judgments. Watchful waiting may be an appropriate strategy for mild episodes of recent onset, but the risks and benefits of this strategy must be assessed in relation to time since onset of the episode.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".