Survey of the National Drug Shortage Effect on Anesthesia and Patient Safety
Bibliographic record
Abstract
BACKGROUND: There are few data on patients' desire to be informed of drug shortages before elective surgery. We surveyed patients who had previously undergone laparoscopic cholecystectomy for their opinions. METHODS: Nine hundred forty-nine Mayo Clinic patients were invited to participate in the survey. The postal survey posed a hypothetical surgical scenario and requested answers regarding the desire to be informed and to postpone scheduled surgery because of neostigmine shortage. Comparison was made with Canadian patients from a hospital in Ontario. RESULTS: Most of the 256 respondents wanted "to be told by the anesthesia doctor about the neostigmine shortage" if there were "slight differences" in side effects between the drug combinations (P < 0.0001). The percentage of patients wanting to know was 76.2% (95% confidence interval, 70.5%-81.3%). Secondary analyses tested the validity and reliability of the survey. With each increase in the differences in substituted drug's side effects, there was a progressive increase in the patients' desire for information (P < 0.0001; 73.2%, 76.2%, and 95.7% of 246, 256, and 253 respondents, respectively) and preference for delaying surgery (P< 0.0001; 33.6%, 39.4%, and 80.9% of 238, 246, and 241 respondents, respectively). There was no association with respondents' sex (P = 0.19), age (P = 0.76), educational level (P = 0.39), or country (United States versus Canada [n = 58]; P = 0.87). CONCLUSIONS: The majority (>50%) of surveyed patients want to be informed of drug shortages that might affect their care.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".