Pediatric sedation in North American children's hospitals: a survey of anesthesia providers
Bibliographic record
Abstract
BACKGROUND: Information about the existence and organization of pediatric sedation services in North America is not available. We conducted a survey to collect this information from anesthesiologists at pediatric institutions and to identify factors perceived as limiting the development of sedation services. METHOD: We electronically mailed a confidential survey about pediatric sedation practice to an attending anesthesiologist involved in pediatric sedation at 116 children's hospitals in the United States and Canada. We identified the institutions using Internet resources. Electronic mailing addresses were obtained from departmental websites, society membership directories and departmental administrators. Our follow-up for nonresponders was by a second e-mail and a telephone call. RESULTS: A total of 54 completed questionnaires were received, a response rate of 47%. Forty-nine (91%) were received from US hospitals, and the remainder from Canadian. Fifty percent of hospitals had a formal pediatric sedation service. Fifty-four percent utilized a 'mobile' provider model. Hospital credentialing for nonanesthesiologist providers varied between 66 and 76% for 'deep' and 'conscious' sedation, respectively. A nurse-physician provider combination was the most common, utilized in 59% of hospitals. Anesthesiologists were the sole sedation providers in 26% of institutions. Propofol was used regularly by nonanesthesiologists for sedation of nonintubated (42%) and intubated (63%) patients. Eighty-seven percent of institutions reported barriers to development of pediatric sedation services. The most common barrier was a shortage of providers, particularly anesthesiologists. CONCLUSIONS: Propofol use by nonanesthesiologists is common. Addressing the shortage of providers, and allocating resources for credentialing providers will encourage further development of pediatric sedation practice.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".