Survival Probabilities and Predictors of Major Depressive Episode Incidence Among Individuals With Various Types of Substance Use Disorders
Bibliographic record
Abstract
This study aimed to estimate the survival probabilities related to the occurrence of major depressive episodes (MDEs) after the onset of substance use disorders (SUDs) using data from the 2012-2013 National Epidemiologic Survey on Alcohol and Related Conditions-III. , Fifth Edition. Individuals with incidents of various SUDs with no prior history of MDEs (n = 5,987 with alcohol use disorder [AUD], 1,353 with cannabis use disorder [CUD], 351 with opioid use disorder [OUD], 827 with stimulant use disorder [STUD], and 5,363 with nicotine use disorder [NUD]) were included. The survival probabilities of these groups were compared to those of a control group without an SUD (n = 20,034). Outcome measures included the number of years from the age at SUD onset until MDE occurrence or the time of the interview. < .0001). Individuals with AUD and STUD, respectively, had a lower and higher probability of having an MDE compared to those with other SUDs. Young age, family history of depression, anxiety disorder presence, and failure to achieve full remission consistently predicted an MDE for all substances. The findings highlight that users of all studied substances have an increased probability of having an MDE over the lifespan.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".