Diagnosis and Treatment of Depression in Patients with Substance Use Disorders
Bibliographic record
Abstract
Case: Jane is a 41 year old married woman who has worked as an administrative assistant for the past 14 years. She began drinking alcohol sporadically at the age of 17. At age 28 her drinking increased, with regular drinking on the weekends of half a bottle of wine on Fridays and Saturdays. Her father died suddenly of a myocardial infarction when she was 35 years old. She describes that her mood began to deteriorate with her father's death when she was 37 years old, followed by progressive loss of interest and isolation. Her 2 children left the home to go to University 2 years later. She reports that her drinking escalated over the last 2–3 years to drinking of a bottle of wine per day with occasionally more on weekends. Her mother has suffered from depression; however, there is no family history of substance problems. Despite recognizing that she needs to cut back or stop her alcohol use, she finds she cannot. She comes to see you complaining mostly of impaired sleep with early morning awakening, but also lethargy, anhedonia, poor concentration, guilt, and passive thoughts of suicide.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".