Multi‐Institution Harmonization of Infection Care Pathways for Pediatric Oncology
Bibliographic record
Abstract
PURPOSE: Care pathways are an implementation tool to help bridge the gap between evidence-based clinical practice guidelines and clinical practice. At four pediatric cancer institutions in Ontario, Canada, institution-specific care pathways for the management of infection complications in pediatric oncology were created. To standardize care delivery approaches across institutions, a project to harmonize the institution-specific care pathways was undertaken. METHODS: The institution-specific infection care pathways were compared. Discrepancies between the pathways were identified, and 33 care pathway components covering 10 clinical actions were prioritized for harmonization. An in-person harmonization meeting with representatives from all institutions was convened, where potential areas for harmonization were identified. At the end of the discussion of each clinical action, the institutional representatives gauged the feasibility of harmonization on a five-point Likert scale. A second virtual meeting was then held to finalize the harmonization plan. RESULTS: Of the 33 care pathway components, harmonization was achieved for 25. Of the 10 components related to antibacterial and antifungal prophylaxis choice, timing, and indications, eight were harmonized. Harmonization was reached for 11 of 16 components related to the initial and ongoing management of febrile neutropenia. Harmonization was achieved for six of the seven components related to prolonged fever. CONCLUSION: Harmonization of infection care pathways across institutions was achieved. However, certain care pathway elements may not be amenable to harmonization due to differences in institutional resources, cultures, and priorities.
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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 | 0.110 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".