Cancellations and delays of emergent orthopedic operations at a Canadian level 1 trauma centre
Bibliographic record
Abstract
Background: Day-of surgery cancellation (DOSC) is considered to be a very inefficient use of hospital resources and results in emotional stress for the patient. To examine opportunities to minimize the incidence of preventable cancellations — an indicator of quality of care — we assessed the incidence of and reasons for DOSCs over 3 months among inpatients and outpatients at a trauma orthopedic service. Methods: This was a prospective study of 2 cohorts of patients, inpatients and outpatients, scheduled for emergent orthopedic surgery at a Canadian tertiary level 1 trauma centre from Jan. 1 to Mar. 31, 2020. Patient demographic characteristics, injury characteristics, delays until surgery and reasons for DOSCs were recorded. Results: A total of 185 patients (100 males and 85 females with a mean age of 54 yr) were included in the study. There were 98 outpatients and 87 inpatients. Seventy-five (40%) of the scheduled procedures in the outpatient group and 34 (30%) of those in the inpatient group were cancelled. In both groups, more than 85% of the cancellations were because of prioritization of a more urgent orthopedic or nonorthopedic surgical case. The average operative delay for the outpatient group was 11.4 days, compared to 3.8 days for the inpatient group (p < 0.001). Conclusion: High DOSC rates were observed among both outpatients and inpatients. The main reason for delaying surgery was prioritization of a more urgent surgical case. Providing the orthopedic trauma service with a dedicated OR opened 6 days per week, along with extended hours of OR services to 1700 daily, might be effective at minimizing DOSCs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".