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Record W2136322495 · doi:10.4212/cjhp.v61i1.11

Safety Issues with Fentanyl Patches Require Pharmaceutical Care

2008· article· en· W2136322495 on OpenAlexvenueaboutno aff
Julie Greenall, Christine Koczmara, Roger Cheng, Sylvia Hyland

Bibliographic record

VenueThe Canadian Journal of Hospital Pharmacy · 2008
Typearticle
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsnot available
Fundersnot available
KeywordsFentanylCoronerMedicineMedical emergencyHarmFamily medicinePharmacologyPoison controlSuicide preventionPolitical scienceLaw

Abstract

fetched live from OpenAlex

In September 2007, the media reported that the chief coroner for Ontario had launched an investigation into 3 patient deaths that appeared to be associated with the use of fentanyl patches.1 The same report noted that “at least 3 more deaths in British Columbia have been linked to the same drug”. A search (on November 26, 2007) of the medication incident database maintained by the Institute for Safe Medication Practices Canada (ISMP Canada) identified 163 reports of incidents involving fentanyl patches, 14 of which had resulted in patient harm, including 1 death. ISMP Canada and its US counterpart, the Institute for Safe Medication Practices (ISMP), have described incidents related to the use of fentanyl patches in several bulletins and have provided recommendations to enhance the safe use of these products.2-5 Manufacturers, Health Canada,6,7 and the US Food and Drug Administration8 have issued advisories and warnings about the use of fentanyl patches. ISMP recently commented that “despite warnings . . . fentanyl transdermal patches continue to be prescribed inappropriately to treat acute pain in opiate-naive patients.”4 The current article contains excerpts (used with permission) from 2 ISMP Canada bulletins describing safety issues related to fentanyl,2,3 including key findings that emphasize the important role that pharmacists can play in reducing the likelihood of harm with this potent analgesic dosage form.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.748
Threshold uncertainty score0.455

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 studies0.0000.000
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 head0.029
GPT teacher head0.303
Teacher spread0.274 · 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

Citations5
Published2008
Admission routes2
Has abstractyes

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