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Record W2084416125 · doi:10.1177/0962280208096046

Efficient sampling approaches to address confounding in database studies

2008· review· en· W2084416125 on OpenAlexaff
James A. Hanley, Nandini Dendukuri

Bibliographic record

VenueStatistical Methods in Medical Research · 2008
Typereview
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsMcGill University Health CentreMcGill University
Fundersnot available
KeywordsConfoundingPharmacoepidemiologyMedical prescriptionComputer scienceSampling (signal processing)MedicineData miningStatisticsMathematicsPharmacology

Abstract

fetched live from OpenAlex

Administrative and other population-based databases are widely used in pharmacoepidemiology to study the unintended effects of medications. They allow investigators to study large case series, and they document prescription medication exposure without having to contact individuals or medical charts, or rely on human recall. However, such databases often lack information on potentially important confounding variables. This review describes some of the sampling approaches and accompanying data-analysis methods that can be used to assess, and deal efficiently with, such confounding.

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)0.059
metaresearch head score (Gemma)0.231
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.949
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0590.231
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.003
Research integrity0.0010.013
Insufficient payload (model declined to judge)0.0020.001

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 head0.915
GPT teacher head0.778
Teacher spread0.137 · 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; both teacher heads agree on what is shown here.

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

Citations9
Published2008
Admission routes1
Has abstractyes

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