The Use of Local Anesthetic Techniques for Closed Forearm Fracture Reduction in Children
Bibliographic record
Abstract
BACKGROUND: Although local anesthetic techniques (hematoma blocks, nerve blocks, intravenous regional anesthesia) for forearm fracture reduction are well described and commonly used in adults, it is unclear how often these techniques are used in children. OBJECTIVE: To characterize the use of local anesthesia for pediatric closed forearm fracture reduction by pediatric and orthopedic physicians practicing in teaching hospitals in Canada and the United States. METHODS: An on-line survey targeting physicians practicing in hospitals with pediatric emergency medicine (PEM) fellowships in Canada and the United States was sent to the PEM fellowship director and orthopedic department head at each hospital. RESULTS: Sixty-three orthopedic surgeons and 69 PEM physicians were invited to participate in the survey, and 63% responded of all invited participants. All respondents routinely use sedation for forearm fracture reduction. Local anesthesia is used by 78% of respondents (55% rarely, 28% sometimes, and 17% frequently). Hematoma blocks are used by 92% of respondents who use local anesthesia; 20% use Bier blocks, and 2% use cubital blocks. Among respondents who never use local anesthesia, all believe that sufficient analgesia is obtained from procedural sedation alone, and 35% believe that local anesthesia is ineffective. CONCLUSIONS: Local anesthetic techniques are used only occasionally by those surveyed. More studies examining the use of local anesthesia for forearm fractures in children are necessary to evaluate the need for more widespread use.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".