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Record W2131308570 · doi:10.1177/0193841x03254349

Outpatient Marijuana Treatment for Adolescents

2003· review· en· W2131308570 on OpenAlexaboutno aff
Michael T. French, M. Christopher Roebuck, Michael L. Dennis, Susan H. Godley, Howard A. Liddle, Frank M. Tims

Bibliographic record

VenueEvaluation Review · 2003
Typereview
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsnot available
FundersNational Institute on Drug AbuseU.S. Public Health Service
KeywordsEconomic evaluationEconomic costEconomic analysisSubstance abuseIntervention (counseling)Drug treatmentQuarter (Canadian coin)MedicineTreatment and control groupsCost–benefit analysisSubstance abuse treatmentCost analysisTotal costEnvironmental healthDemographyPsychiatryEconomicsInternal medicineAgricultural economicsGeographyPolitical scienceOperations research

Abstract

fetched live from OpenAlex

An economic evaluation of five outpatient adolescent treatment approaches (12 total site-by-conditions) was conducted. The economic cost of each of the 12 site-specific treatment conditions was determined by the Drug Abuse Treatment Cost Analysis Program (DATCAP). Economic benefits of treatment were estimated by first monetizing a series of treatment outcomes and then analyzing the magnitude of these monetized outcomes from baseline through the 12-month follow-up. The average economic costs ranged from $90 to $313 per week and from $839 to $3,279 per episode. Relative to the quarter before intake, the average quarterly cost to society for the next 12 months (including treatment costs) significantly declined in 4 of the 12 site-by-treatment conditions, remained unchanged in 6 conditions, and increased in 2 treatment conditions (both in the same site). These results suggest that some types of substance-abuse intervention for adolescents can reduce social costs immediately after treatment.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.170
GPT teacher head0.451
Teacher spread0.281 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations60
Published2003
Admission routes1
Has abstractyes

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