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Record W2119030200 · doi:10.1002/0470011815.b2a04017

Drug Utilization Patterns

2005· other· en· W2119030200 on OpenAlex

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

VenueEncyclopedia of Biostatistics · 2005
Typeother
Languageen
FieldMathematics
TopicAdvanced Causal Inference Techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsObservational studyData collectionNonparametric statisticsDrugComputer scienceData scienceMedicineEconometricsPharmacologyStatisticsEconomicsMathematics

Abstract

fetched live from OpenAlex

Abstract We review methodological challenges in the observational studies of the patterns and outcomes of drug utilization. First, we address issues related to design and data collection in different studies that rely on administrative databases, discuss their limitations in accounting for clinical indication bias, and outline new opportunities created by the emergence of the integrated electronic patient records. Then, we focus on the different analytical issues related to such complexities as prescriber effects, assessing the impact of policy changes on drug utilization, description and classification of longitudinal patterns of medication use, alternative methods of measuring time‐dependent exposure status and medication dose, use of nonparametric modeling to represent time‐varying, lagged and/or nonlinear effects of exposure.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.033
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0050.000

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 head0.072
GPT teacher head0.382
Teacher spread0.310 · 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