Prioritizing Elective Surgical Cases During a Pandemic or Global Crisis: The Elective-Pediatric Orthopedic Surgical Timing (E-POST) Score
Bibliographic record
Abstract
BACKGROUND: As the first wave of the COVID-19 pandemic stabilized and resources became more readily available, elective surgery was reinitiated and hospitals realized that there was little guidance on how to prioritize elective cases. METHODS: A prioritization tool was formulated based on clinically relevant elements and previous literature. Nine pediatric orthopaedic surgeons from North American institutions evaluated 25 clinical scenarios on 2 occasions separated in time. Intra-rater and inter-rater reliability were calculated [intraclass correlation coefficient (ICC)]. Surgeons also ranked the importance of each element and how confident they were with scoring each factor. RESULTS: Intra-rater ICC for total score showed good to excellent consistency; highest at 0.961 for length of stay (LOS) and lowest at 0.705 for acuity. Inter-rater ICC showed good to excellent agreement for American Society of Anesthesiologists score, LOS, duration of surgery, and transfusion risk and moderate agreement for surgical acuity and personal protective equipment (PPE) use. Transfusion risk and duration of surgery were deemed least important, and surgeons were least confident in scoring PPE and transfusion risk. Based on findings, the novel Elective-Pediatric Orthopedic Surgical Timing (E-POST) score for prioritizing elective cases was developed, consisting of 5 factors: surgical acuity, global health status, LOS, duration of surgery, and PPE requirement. CONCLUSIONS: The E-POST numeric total score or subscore may help objectively prioritize elective cases during a global crisis. LEVEL OF EVIDENCE: Level V.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".