Current management of tibial shaft fractures: A survey of 450 Canadian orthopedic trauma surgeons
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Strategies to manage tibial fractures include nonoperative and operative approaches. Strategies to enhance healing include a variety of bone stimulators. It is not known what forms of management for tibial fractures predominate among Canadian orthopedic surgeons. We therefore asked a representative sample of orthopedic trauma surgeons about their management of tibial fracture patients. METHODS: This was a cross-sectional survey of 450 Canadian orthopedic trauma surgeons. We inquired about demographic variables and current tibial shaft fracture management strategies. RESULTS: 268 surgeons completed the survey, a response rate of 60%. Most respondents (80%) managed closed tibial shaft fracture operatively; 47% preferred reamed intramedullary nailing and 40% preferred unreamed. For open tibial shaft fractures, 59% of surgeons preferred reamed intramedullary nailing. Some surgeons (16%) reported use of bone stimulators for management of uncomplicated open and closed tibial shaft fractures, and almost half (45%) made use of this adjunctive modality for complicated tibial shaft fractures. Low-intensity pulsed ultrasound and electrical stimulation proved equally popular (21% each) and 80% of respondents felt that a reduction in healing time of 6 weeks or more, attributed to a bone stimulator, would be clinically important. INTERPRETATION: Current practice regarding orthopedic management of tibial shaft fractures in Canada strongly favors operative treatment with intramedullary nailing, although respondents were divided in their preference for reamed and unreamed nailing. Use of bone stimulators is common as an adjunctive modality in this injury population. Large randomized trials are needed to provide better evidence to guide clinical decision making regarding the choice of reamed or unreamed nailing for tibial shaft fractures, and to inform surgeons about the actual effect of bone stimulators.
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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 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".