Clinical Pathways Programs in Children’s Hospitals
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Clinical pathways translate best evidence into the local context of a care setting through structured, multidisciplinary care plans. Little is known about clinical pathway programs in pediatric settings. The purpose of this study was to determine the prevalence of clinical pathway programs and describe similarities and differences. METHODS: We performed a cross-sectional web survey to assess the existence of a clinical pathway program, number, type, and creation or revision of clinical pathways, and its characteristics in the 111 hospitals of the Pediatric Research in Inpatient Settings network. RESULTS: Eighty-one hospitals responded to the survey (73% response rate). Most hospitals had a clinical pathway program (63%, n = 50 of 80) that was hospital-wide (70%, n = 35 of 50). Freestanding children's (48%, n = 39 of 81), academic (60%, n = 43 of 72), teaching hospitals (96%, n = 78 of 81) made up the largest proportion of survey respondents. There was no funding for nearly half of the programs (n = 21 of 46, 46%). Over a quarter of survey respondents reported no data collected to assess pathway utilization and/or care outcomes (n = 19 of 71, 27%). CONCLUSIONS: Greater than half of respondents confirmed existence of a program. Freestanding, academic teaching hospitals accounted for the most responses. However, nearly half of surveyed programs were unfunded, and many are unable to measure their pathway outcomes or demonstrate improvement in care. Survey respondents were enthusiastic about participating in a national collaborative on pediatric clinical pathways.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".