Bibliographic record
Abstract
BACKGROUND: This case report describes a patient who developed severe bradycardia due to transdermal fentanyl. There have been no prior case reports of this occurring in palliative care, but the frequency of association of fentanyl with bradycardia in the anesthesia setting suggests it may be more common than realized. Palliative care settings often have a policy of not routinely checking vital signs, and symptoms of bradycardia could be misinterpreted as the dying process. CASE PRESENTATION: A patient with recurrent ovarian cancer was admitted with nausea and abdominal pain due to bowel obstruction and fever from a urinary tract infection. A switch from injectable hydromorphone to transdermal fentanyl resulted in symptomatic severe bradycardia within 36 h, without any other signs of opioid toxicity and with good analgesic effect. CASE MANAGEMENT: The fentanyl patch was removed. Atropine was not required. CASE OUTCOME: The patient made an uneventful recovery. Transdermal buprenorphine was subsequently used satisfactorily for long-term background pain control, with additional hydromorphone when needed. CONCLUSIONS: The delayed absorption of fentanyl via the transdermal route makes early identification of fentanyl-induced bradycardia key to prompt reversal. Patients with resting or relative bradycardia may be at higher than average risk.
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 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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".