Creation of an enhanced recovery after surgery protocol for children with Wilms tumours in low- and middle-income countries
Bibliographic record
Abstract
Enhanced Recovery After Surgery guidelines have been developed and shown to improve outcomes for many surgical procedures. Most existing guidelines have been created for patients in high-resource settings. There is a dearth of guidelines for pediatric populations particularly in low- and middle-income countries (LMIC). All children with Wilms tumours require resection for cure and therefore a perioperative care pathway can streamline care for all curative-intent nephrectomies. A two-round Delphi consensus of clinicians in LMICs was utilized to determine the scope and content of recommendations for an enhanced recovery protocol for children with Wilms tumours undergoing nephrectomy in LMICs. Consensus was predefined a priori as ≥70% of panelists indicating a topic or recommendation should be included. Twenty-six topics met consensus for inclusion and were consolidated into twenty recommendations for implementation in the preoperative, intraoperative, and postoperative setting. Predominant themes included perioperative nutrition, surgical safety, anesthetic concerns, and interdisciplinary oncology care. Ten participants completed round one and six completed round two. All recommendations met consensus for inclusion after two rounds. A consensus-derived perioperative care pathway for children with Wilms tumours in low-resource settings is presented. Recommendations share many priorities with high-resource pathways, but also contain unique considerations for a low-resource setting.
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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 | 0.107 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".