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Record W4205306034 · doi:10.1016/j.cjco.2021.12.012

The Problem With Predictions: A Cautionary Tale of Empirically Adjusting Apixaban Dosing With Carbamazepine

2022· article· en· W4205306034 on OpenAlexaff
Ayush Chadha, David G. Lopaschuk, Margaret L. Ackman, Tammy J. Bungard

Bibliographic record

VenueCJC Open · 2022
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsAlberta Health ServicesUniversity of Alberta
FundersPfizer
KeywordsCarbamazepineDosingApixabanComputer scienceMedicineEconometricsMathematicsPharmacologyInternal medicineEpilepsyWarfarinPsychiatryRivaroxaban

Abstract

fetched live from OpenAlex

Concomitant use of apixaban and carbamazepine (CBZ) is not recommended due to an anticipated reduction in apixaban concentration, although few case reports describe this interaction. We report a case of initiating apixaban 10 mg twice daily (BID), in a patient stabilized on CBZ 600 mg BID that was guided by prior experience. Apixaban concentrations were substantially elevated with initial empiric dosing; apixaban dosing of 7.5 mg BID was eventually implemented. This case highlights the fact that the degree of induction by CBZ can vary, regardless of the dose, and requires clinicians to be cautious when applying prior experiences with patients to new patients.

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 score0.257
Threshold uncertainty score0.468

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 studies0.0010.000
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 head0.315
Teacher spread0.289 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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