The Impact of Peripheral Nerve Block on the Quality of Care After Ankle Fracture Surgery: A Quality Improvement Study
Bibliographic record
Abstract
OBJECTIVES: To increase peripheral nerve block (PNB) administration for ankle fracture surgeries (AFSs) at our institution to above 50% by January 1st, 2021. DESIGN: Longitudinal, single-center quality improvement study conducted at a high-volume tertiary care center. PATIENTS: All patients undergoing isolated AFS for unimalleolar, bimalleolar, or trimalleolar ankle fracture from July 2017 to April 2021 were included in this study. INTERVENTION: Interventions implemented to minimize barriers for PNB administration included recruitment and training of expert anesthesiologists in regional anesthesia, procurement of ultrasound machines, implementation of a dedicated block room, and creation of a pamphlet for patients describing multimodal analgesia. MAIN OUTCOME MEASUREMENT: The primary outcome was the percentage of patients receiving PNB for AFS. Secondary outcomes included hospital length-of-stay, postanesthesia care unit (PACU) and 24-hour postoperative opioid consumption (mean oral morphine equivalent [OME]), proportion of patients not requiring opioid analgesic in PACU, and PACU and 24-hour postoperative nausea/vomiting requiring antiemetic. RESULTS: The PNB and non-PNB groups included 78 and 157 patients, respectively. PNB administration increased from <5% to 53% after implementation of the improvement bundle. Mean PACU and 24-hour opioid analgesic consumption was lower in the PNB group (PACU OME 38.96 mg vs. 55.42 mg, P = 0.001; 24-hour OME 50.83 mg vs. 65.69 mg, P = 0.008). A greater proportion of patients in the PNB group did not require PACU opioids (62.8% vs. 27.4%, P < 0.001). CONCLUSIONS: By performing a root cause analysis and implementing a multidisciplinary, patient-centered improvement bundle, we increased PNB administration for AFSs, resulting in reduced postoperative opioid analgesia consumption. LEVEL OF EVIDENCE: Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".