Safety Issues with Fentanyl Patches Require Pharmaceutical Care
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".