Pharmacovigilance in Hospice/Palliative Care: Net Effect of Haloperidol for Nausea or Vomiting
Bibliographic record
Abstract
BACKGROUND: Haloperidol is widely prescribed as an antiemetic in patients receiving palliative care, but there is limited evidence to support and refine its use. OBJECTIVE: To explore the immediate and short-term net clinical effects of haloperidol when treating nausea and/or vomiting in palliative care patients. DESIGN: A prospective, multicenter, consecutive case series. SETTING/SUBJECTS: Twenty-two sites, five countries: consultative, ambulatory, and inpatient services. MEASUREMENTS: When haloperidol was started in routine care as an antiemetic, data were collected at three time points: baseline; 48 hours (benefits); day seven (harms). Clinical effects were assessed using the National Cancer Institute's Common Terminology Criteria for Adverse Events (NCI CTCAE). RESULTS: Data were collected (May 2014-March 2016) from 150 patients: 61% male; 86% with cancer; mean age 72 (standard deviation 11) years and median Australian-modified Karnofsky Performance Scale 50 (range 10-90). At baseline, nausea was moderate (88; 62%) or severe (11; 8%); 145 patients reported vomiting, with a baseline NCI CTCAE vomiting score of 1.0. The median (range) dose of haloperidol was 1.5 mg/24 hours (0.5-5 mg/24 hours) given orally or parenterally. Five patients (3%) died before further data collection. At 48 hours, 114 patients (79%) had complete resolution of their nausea and vomiting, with greater benefit seen in the resolution of nausea than vomiting. At day seven, 37 (26%) patients had a total of 62 mild/moderate harms including constipation 25 (40%); dry mouth 13 (21%); and somnolence 12 (19%). CONCLUSIONS: Haloperidol as an antiemetic provided rapid net clinical benefit with low-grade, short-term harms.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".