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Record W2408467563 · doi:10.1055/s-0029-1223424

Heroingestützte Behandlung in der Schweiz im Langzeitverlauf 1994–2007: Einflussfaktoren auf den Behandlungserfolg

2010· article· de· W2408467563 on OpenAlex
Ulrich Frick, Wolfgang Wiedermann, Michael Schaub, Ambros Uchtenhagen, Jürgen Rehm

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePsychiatrische Praxis · 2010
Typearticle
Languagede
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsMedicineGynecology

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0030.009

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.

Opus teacher head0.018
GPT teacher head0.326
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it