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Record W2254901870 · doi:10.3233/jrs-150662

High risk use of OTC NSAIDs and ASA in family medicine: A retrospective chart review

2015· article· en· W2254901870 on OpenAlexafffund
Kevin Hamilton, Christine S. Davis, Jamie Falk, Alexander Singer, Shawn Bugden

Bibliographic record

VenueInternational Journal of Risk & Safety in Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicInflammatory mediators and NSAID effects
Canadian institutionsUniversity of Manitoba
FundersUniversity of Manitoba
KeywordsMedicineMedical prescriptionOver-the-counterRetrospective cohort studyMedical recordInternal medicineEmergency medicineIntensive care medicinePharmacology

Abstract

fetched live from OpenAlex

BACKGROUND: Complications associated with the use of NSAIDs, antiplatelet agents, and anticoagulants are among the top causes of preventable drug-related ER visits, hospitalizations and death. Although over-the-counter (OTC) NSAIDs and ASA also contribute to this preventable risk, it is unclear how well these medications are documented in primary care records. METHODS: A retrospective electronic and paper chart review was conducted to evaluate the prevalence of 13 evidence-based high-risk prescriptions and the contribution of OTC NSAIDs and ASA to these potentially inappropriate prescriptions (PIPs). RESULTS: Of the 148 patients included in the review, ASA was taken by 117 patients (79%) while OTC NSAIDs were taken by 36 (24%). OTC NSAIDs were never documented within the "medication" section of the electronic record, whereas ASA was documented in 65 (56%) cases. Eighty percent (118/148) taking either OTC NSAIDs or ASA were identified as having at least one PIP. CONCLUSION: OTC NSAIDs and ASA are widely available and are commonly taken without the knowledge of the prescriber. These medications contribute to the overall risk of bleeding. Review and documentation of OTC NSAIDs and ASA use should be part of all relevant patient encounters when prescribing NSAIDs, antiplatelets and anticoagulants.

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)0.006
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.029
GPT teacher head0.323
Teacher spread0.294 · 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 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

Citations9
Published2015
Admission routes2
Has abstractyes

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