Bibliographic record
Abstract
OBJECTIVE: Pediatric sedation practices vary among institutions and even within the same institution depending on providers and location. We planned to implement a pediatric procedural sedation program for a tertiary care pediatric emergency department to standardize sedation practices among emergency physicians. METHODS: An interactive contextual planning model was adapted, and several tasks were initiated simultaneously. The director of pediatric emergency medicine and clinical director of the institution approved the proposal for the sedation program. Needs assessment surveys and focus group interviews were conducted to identify educational needs of the target audience and infuse a sense of ownership. A grant was obtained from the institution because the budget exceeded available divisional funds. Other pediatric sedation guidelines and published literature were used to produce a sedation handbook and pocket card. Interim approval was obtained from the Drugs and Therapeutics Committee and the Patient Care Committee. RESULTS: The program was successfully implemented after all physicians and nurses working in the emergency department attended a half-day sedation course and completed a multiple-choice examination. Random chart audits verify that the emergency physicians are performing almost all procedural sedations now as per protocol. CONCLUSIONS: Implementing a structured program facilitates guideline adherence. Adapting a flexible contextual planning model was successful in translating guidelines to practice where resources were limited, and the target audience was highly trained adult learners.
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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 | 0.017 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.050 | 0.037 |
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".