Prognostic factors for predicting health-related quality of life after intramedullary nailing of tibial fractures: a randomized controlled trial
Bibliographic record
Abstract
AIMS: Using tibial shaft fracture participants from a large, multicentre randomized controlled trial, we investigated if patient and surgical factors were associated with health-related quality of life (HRQoL) at one year post-surgery. METHODS: The Study to Prospectively Evaluate Reamed Intramedullary Nails in Patients with Tibial Fractures (SPRINT) trial examined adults with an open or closed tibial shaft fracture who were treated with either reamed or unreamed intramedullary nails. HRQoL was assessed at hospital discharge (for pre-injury level) and at 12 months post-fracture using the Short Musculoskeletal Functional Assessment (SMFA) Dysfunction, SMFA Bother, 36-Item Short Form 36 (SF-36) Physical, and SF-36 Mental Component scores. We used multiple linear regression analysis to determine if baseline and surgical factors, as well as post-intervention procedures within one year of fracture, were associated with these HRQoL outcomes. Significance was set at p < 0.01. We hypothesize that, irrespective of the four measures used, prognosis is guided by both modifiable and non-modifiable factors and that patients do not return to their pre-injury level of function, nor HRQoL. RESULTS: For patient and surgical factors, only pre-injury quality of life and isolated fracture showed a statistical effect on all four HRQoL outcomes, while high-energy injury mechanism, smoking, and race or ethnicity, demonstrated statistical significance for three of the four HRQoL outcomes. Patients who did not require reoperation in response to infection, the need for bone grafts, and/or the need for implant exchanges had statistically superior HRQoL outcomes than those who did require intervention within one year after initial tibial fracture nailing. CONCLUSION: 2021;2(1):22-32.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".