Heroingestützte Behandlung in der Schweiz im Langzeitverlauf 1994–2007: Einflussfaktoren auf den Behandlungserfolg
Bibliographic record
Abstract
OBJECTIVE: To identify prognostic factors for a positive or negative termination of heroin-assisted treatment (HAT) in Switzerland. METHOD: A complete census of all 3155 patients ever admitted was analysed using the proportional hazard model (including time dependent covariates). RESULTS: Median length of stay was 11.4 years; the maximal length of stay was 13.9 years. 299 positive and 463 negative terminations were registered. Terminations clustered in the first year. Both time to positive and negative termination was significantly dependent on historical treatment cohorts since 1994. Positive termination was negatively associated with treatment in larger treatment centres (OR: 0.77, CI: 0.61-0.97) and positively with income from the social system (OR: 1.33; CI: 1.03-1.72). Negative terminations were positively associated with HIV infection before treatment (OR: 1.74; CI: 1.40-2.16), delinquence (OR 1.36; CI: 1.09-1.69), and higher levels of distrust (OR: 1.18 per scoring point; CI = 1.05-1.31). CONCLUSIONS: Length of stay in Swiss HAT is considerable. The proportion of positive terminations did not increase with longer stays, indicating that the majority of patients are in chronic palliative care. Negative terminations outweighed positive terminations, with a low predictive power from co-variates. The routine assessment and analysis of different covariates, such as indicators of treatment process, has the potential to improve the therapeutic outcomes of HAT.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".