Bibliographic record
Abstract
Moderate to weak evidence suggests that patients with substance use disorders who received residential treatment were more likely than outpatients to complete treatment and be considered abstinent. Comparisons between residential treatment and outpatient programs for other outcomes were unclear. Strong- to weak-quality evidence showed that residential treatment services for patients with substance use disorders was effective in improving various outcomes including substance use, social, criminal activity, and mental health outcomes. However, residential treatment was likely associated with poorest survival outcomes after discharge compared to other treatments. Managed alcohol programs in hospital settings appeared to be effective and safe in preventing and treating alcohol withdrawal syndrome in surgical patients, trauma patients, or hospitalized patients. The level of evidence was not assessed. There was evidence that managed alcohol programs in community settings improved drinking patterns, alcohol-related harm, criminal activity, mental health, and social and physical well-being. The level of evidence was not assessed. The American Society of Addiction Medicine clinical practice guideline provides recommendations for the identification and management of alcohol withdrawal in inpatient and ambulatory settings. Patients’ current signs and symptoms, levels of risk for developing severe or complicated withdrawal or complications of withdrawal, and other dimensions should be taken into consideration in the assessment process to determine the appropriate level of care. Strength of recommendations was not assessed. The Canadian Coalition for Seniors’ Mental Health recommends that patients with cannabis use disorder should be considered for residential treatment if they are unable to effectively reduce or cease their cannabis use (level of evidence: Low; strength of recommendation: Strong).
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 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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.049 | 0.003 |
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".