A 10-year review of pain management practices for rib fractures at a lead trauma hospital: Are we adopting all multimodal pain management strategies?
Bibliographic record
Abstract
Objective Rib fractures lead to altered breathing mechanics, impaired gas exchange, and in some patients to respiratory failure. Multimodal analgesia has become one of the pillars of management; however, barriers toward widespread adoption remain. We reviewed the trends in multimodal pain management strategies and their drivers over a 10-year period at a lead trauma hospital in Canada. Methods This is a cross-sectional study in which demographic, injury, pharmacological, as well as outcome data were collected for all adult patients admitted with rib fractures. Data were collected retrospectively and the study period was divided into three eras (2011–2014; 2015–2018; 2019–2022). Multimodal pain management was defined as either patient-controlled (PCA) or regional analgesia (epidural or continuous plane block) in combination with acetaminophen or non-steroidal anti-inflammatory drugs (NSAIDs). All patients received pro re nata (PRN) narcotics. Data were stratified based on rib fracture severity, age, and intubation on arrival. Results We identified 2586 patients with rib fractures (mild non-flail 32.6%; moderate non-flail 50.2%; flail 17.2%). The use of standing acetaminophen and NSAIDs increased over time for all groups. Regional analgesia use increased while PCA use reduced, particularly in the flail group. Only 7% of flail patients intubated on arrival received regional analgesia. Among those not intubated on arrival, only 35% of flail and 23% of non-flail patients received multimodal pain management. Over time, multimodal pain management decreased in the non-flail group due to a reduction in PCA use without an equal rise in regional anesthesia. Those without multimodal pain management were older and the mechanism of injury was more likely a fall. Among those with a flail, who were non-intubated at presentation, only 31% of those aged >65 received multimodal pain regimens compared to 45% in those ≤65y. Conclusions Although multimodal pain management strategies have improved over time, a large proportion of patients, even among those with flail chest, still do not receive multimodal pain management. Elderly patients, at highest risk of adverse outcomes, were less likely to receive multimodal pain management strategies and should be the target of performance improvement initiatives.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".