Surgeon-estimated costs of common consumables in otolaryngology
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: The current fiscal climate demands increasing emphasis on curbing hospital expenses incurred from surgical procedures. Disposable instruments and consumables play a major role, but the end user (the surgeon) is often unaware of the cost of these materials. The objectives of our study were: 1) to assess knowledge of costs of disposable instruments and consumable products, and 2) to gauge interest in greater access to cost information and its potential to change practice. STUDY DESIGN: We used a cross-sectional survey study to meet our study's objectives. METHODS: A paper-based anonymous questionnaire was administered in the Department of Otolaryngology at McGill University and at Western University asking for estimations of costs of 23 commonly used products in the operating room. Our primary outcome measure was accuracy of cost estimations, which were considered accurate if within ± 50% of the true cost at the respective institution. RESULTS: The average accuracy was 29.9% (standard deviation = 16.7%). There was no significant difference between residents (32.5%, 95% confidence interval [CI]: 10.2%-54.7%) and staff (28.3%, 95% CI: 11.0%-45.6%). Less than 10% of participants were able to accurately estimate the costs of at least half of the disposable products. The majority of participants (82%) felt that greater information would change their use of consumables. CONCLUSIONS: Surgical residents and staff have a generally poor knowledge of the cost of common consumable products used in the operating room. There is potential for increased awareness of costs to change behavior. LEVEL OF EVIDENCE: NA.
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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 | 0.005 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".