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Record W2161393399 · doi:10.3109/10826084.2012.706165

Premature Death as the Ultimate Failure: Predictors of Death in the US Drug User Treatment Population

2012· article· en· W2161393399 on OpenAlexaff
Sam Schildhaus, Bernard L. Dugoni

Bibliographic record

VenueSubstance Use & Misuse · 2012
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsThomson Reuters (Canada)
Fundersnot available
KeywordsDemographyLogistic regressionMedicineDrug userPopulationRace (biology)Public healthDrugMedical emergencyPsychiatryInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Premature death is the ultimate failure in public health. Failure to complete substance user treatment increases the likelihood of death. Using the five-year follow-up (1990/91-1995/96) of a representative sample of 3,047 clients discharged from drug user treatment, this article documents that deaths after treatment were 4.7 times higher for substance user treatment clients than for the U.S. population matched by age, sex, and race; death rates ranged from 3.5 times as likely for Black males to nine times as likely for White females. Logistic regression models show that completion of treatment is associated with a three-fifths decreased likelihood of death.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.727

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.288
Teacher spread0.261 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2012
Admission routes1
Has abstractyes

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