A Canadian Weekend Elective Pediatric Surgery Program to Reduce the COVID-19–Related Backlog: Operating Room Ramp-Up After COVID-19 Lockdown Ends—Extra Lists (ORRACLE-Xtra) Implementation Study (Preprint)
Bibliographic record
Abstract
BACKGROUND The COVID-19 pandemic caused by the SARS-COV-2 virus has resulted in unprecedented challenges for the health care system. A decrease of surgical services led to substantial backlogs for time-sensitive scheduled pediatric patients. We designed and implemented a novel pilot weekend surgical quality improvement project called Operating Room Ramp-Up After COVID Lockdown Ends—Extra Lists (ORRACLE-Xtra). OBJECTIVE Our overall goals are to increase patient access to surgery (and reduce the wait list), improve operating room efficiencies, and optimize parent and staff experience. METHODS Using the DMAIC (define, measure, analyze, improve, control) framework, we implemented ORRACLE-Xtra in a tertiary care academic pediatric hospital during a quiescent period of the COVID-19 pandemic. We defined process and outcome measures based on provincial targets of out-of-window cases. Parental and staff satisfaction was tracked by surveys. RESULTS ORRACLE-Xtra led to 247 patients receiving surgery during the pilot period, resulting in a 5% decrease in the total number of patients on our wait list with Paediatric Canadian Access Targets for Surgery IV (147/247, 59.5%), with 38.1% (94/247) out-of-window of provincial targets. Most of the process and outcome measures were met or exceeded. Overall parental satisfaction was at 95.8% (110/121), with 79% (64/81) of staff reporting satisfaction with working weekends. CONCLUSIONS Through the ORRACLE-Xtra pilot program, we have shown that hospitals impacted by COVID-19 can reduce the surgical backlog using innovative models of service delivery in a Canadian context. Sustained funding is critical to achieving more meaningful reductions in wait times for scheduled surgeries over the longer term and needs to be balanced with staff well-being.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".