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Record W2980883791 · doi:10.1002/cpt.1698

Reproducing Protocol‐Based Studies Using Parameterizable Tools—Comparison of Analytic Approaches Used by Two Medical Product Surveillance Networks

2019· article· en· W2980883791 on OpenAlexafffundabout
Ting‐Ying Huang, Emily C. Welch, Mayura Shinde, Robert W. Platt, Kristian B. Filion, Laurent Azoulay, Judith C. Maro, Richard Platt, Sengwee Toh

Bibliographic record

VenueClinical Pharmacology & Therapeutics · 2019
Typearticle
Languageen
FieldMathematics
TopicAdvanced Causal Inference Techniques
Canadian institutionsCanadian Society of NephrologyInstitut universitaire en santé mentale de MontréalJewish General HospitalMcGill University
FundersCanadian Institutes of Health ResearchU.S. Food and Drug Administration
KeywordsProtocol (science)MedicineObservational studyHazardAcute pancreatitisEmergency medicinePharmacologyComputer scienceInternal medicinePathologyAlternative medicine

Abstract

fetched live from OpenAlex

The US Sentinel System and the Canadian Network for Observational Drug Effect Studies (CNODES) are two medical product safety surveillance networks. Using Sentinel's preprogrammed, parameterizable analytic tools, we reproduced two protocol-based studies conducted by CNODES to assess the risks of acute pancreatitis and heart failure (HF) associated with the use of incretin-based drugs, compared with use of ≥ 2 oral hypoglycemic agents. Results from the replication new-user cohort analyses aligned with those from the CNODES nested case-control studies. The adjusted hazard ratios were 0.95 (0.81-1.12; vs. 1.03 (0.87-1.22) in CNODES) for acute pancreatitis and 0.91 (0.84-1.00; vs. 0.82 (0.67-1.00) in CNODES) for HF among patients without HF history. The CNODES's common protocol approach allows studies tailored to specific safety questions, whereas the Sentinel's common data model plus pretested program approach enables more rapid analysis. Despite these differences, it is possible to obtain comparable results using both approaches.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.788
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.722
GPT teacher head0.622
Teacher spread0.100 · 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.

Study designSimulation or modeling
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

Citations6
Published2019
Admission routes3
Has abstractyes

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