Survey of Current Practices: Peripheral Nerve Block Utilization by ED Physicians for Treatment of Pain in the Hip Fracture Patient Population
Bibliographic record
Abstract
BACKGROUND: In 2010-11 approximately 968 hip fracture patients presented to emergency departments in the Greater Toronto Local Health Integration Network (GTA-LHIN). Optimal pain management is a frequently overlooked aspect of hip fracture patient care, which may contribute to patient outcomes. Although recommendations have been published, there is currently not a standardized approach to the analgesic management of pain in the hip fracture patient. Nerve blocks, including the fascia iliaca compartment block (FICB), are more effective than traditional opioid analgesics in reducing pain after hip fracture. Research suggests that analgesia via nerve blockade is best initiated early, upon arrival to the emergency department. Emergency physicians are trained in ultrasound, and do utilize regional anaesthesia; however, the frequency of block utilization and techniques used for block insertion are unknown. We sought to undertake the first survey of Emergency Department (ED) staff and resident physicians across the GTA-LHIN, looking at the current ED practice of nerve block analgesia in hip fracture patients. PURPOSE: The primary aim was to determine the prevalence and range of techniques utilized. The secondary aims were to determine the extent of training in nerve block insertion techniques, to gauge opinion on the most important objectives for future training courses, and to seek an understanding of the barriers to establishing a standardized approach for nerve block utilization in hip fracture patients. CONCLUSIONS: This data will be used to develop a multidisciplinary training program specifically for use by ED physicians. ED physicians and anesthesiologists will collaborate to standardize nerve block insertion techniques and develop an optimal analgesic management plan of hip fracture patients at Sunnybrook Hospital.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".