Starting on the road to pediatric enhanced recovery after surgery: strategies and themes
Bibliographic record
Abstract
ABSTRACT: Like many innovations in surgery, implementing enhanced recovery after surgery (ERAS) programs in pediatric surgery has lagged behind its adult counterpart. Our institutions have recently made a concerted effort to introduce and implement ERAS widely throughout the Department of Pediatric Surgery, which includes 10 surgical specialties. Our strategy revolved around the early inclusion of all stakeholders, significant educational efforts, and creating a roadmap for development and implementation of ERAS programs. It culminated in the first Canadian pediatric ERAS conference, which was held in Montreal on April 28, 2023, and served as a launching pad for instituting an ERAS culture in our hospital. Throughout the process, we identified specific themes that helped emphasize the rationale for pediatric ERAS programs and kept stakeholders engaged in the efforts. In this paper, we share these strategies and themes, believing they can aid other pediatric institutions seeking to initiate an ERAS culture.Level of evidence: 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 | 0.029 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".