Pediatric anesthesia in North America
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: This educational review outlines the current landscape of pediatric anesthesia training, care delivery, and challenges across Canada, Barbados, and the United States. DESCRIPTIONS AND CONCLUSIONS: Approximately 5% of Canadian children undergo general anesthesia annually, administered by fellowship-trained pediatric anesthesiologists in children's hospitals, general anesthesiologists in community hospitals, or family practice anesthesiologists in underserved regions. In Canada, the focus is on national-level evaluation and accreditation of pediatric anesthesia fellowship training, addressing challenges arising from workforce shortages, particularly in remote areas. Barbados, a Caribbean nation, lacks dedicated pediatric hospitals but has provided pediatric anesthesia since 1972 through anesthetists with additional training. Challenges in its development, common to low-middle-income countries, include inadequate infrastructure and workforce shortages. Increased awareness of pediatric anesthesia as a sub-specialty could enhance perioperative care for Barbadian children. Pediatric anesthesia encompasses various specialties in the United States, with pediatric anesthesiologists playing a foundational role. Challenges faced include recruitment and retention difficulties, supply-chain shortages, and the proliferation of anesthesia sites, all impacting the delivery of modern, high-quality, and cost-effective patient care. Collaborative efforts at national and organizational levels strive to improve the quality and safety of pediatric anesthesia care in the United States.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".