Exploring Canadian surgeons' decisions about postoperative weight bearing for their hip fracture patients
Bibliographic record
Abstract
For older adults with osteoporosis, a fall resulting in hip fracture is a life-changing event from which only one-third fully recover. Current best evidence argues strongly for elderly patients to bear weight on their repaired hip fracture immediately after their surgery to maximize their chances of full or nearly full recovery. Patient stakeholders in Canada have argued that some surgeons fail to issue "weight-bearing-as-tolerated" (WBAT) orders in all eligible cases, protecting their bony repair but contributing to increased mortality and long-term disability rates. In collaboration with a national stakeholder organization, Bone and Joint Canada, we interviewed 20 orthopedic surgeons across Canada who perform hip fracture repair surgery, with the aim of understanding their attitudes and behavior toward patient management regarding weight bearing. Qualitative content analysis, in which themes are identified and agreed by multiple coders, suggested that both patient characteristics and surgeon factors influence surgeons' postoperative weight-bearing orders. While almost all respondents agreed that weight bearing as tolerated is indeed therapeutic for most hip fracture repair or replacement patients, surgeons also described certain patient characteristics that would diminish the value of immediate weight bearing, including poor bone quality and certain types of fracture pattern. Surgeon factors that affect postoperative mobilization orders include choice of construct, previous experience of construct failure, and lack of local audit data regarding past weight-bearing decisions and patient outcomes. Thus, although familiar with best practice guidelines, surgeons also have "rules to break the rules." In an era when "good" medicine leans toward science rather than art, the role of individual experience in decision making with regard to hip fracture care continues to be important and would benefit from being discussed openly.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".