Risk Factors for the Development of Persistent Scaphoid Non-Union After Surgery for an Established Non-Union
Bibliographic record
Abstract
BACKGROUND: Between 2014 and 2020, candidates for scaphoid non-union (SNU) surgery were enrolled in a prospective randomized trial (Scaphoid Nonunion and Low Intensity Pulsed Ultrasound [SNAPU] trial) evaluating the effect of low-intensity pulsed ultrasound on postoperative scaphoid healing. At trial completion, 114/134 (85%) of these patients went on to union, and 20/134 (15%) went on to persistent SNU (PSNU). The purpose of this study was to use this prospectively gathered data to identify patient-, fracture-, and surgery-specific risk factors that may be predictive of PSNU in patients who undergo surgery for SNU. METHODS: . A stepwise multivariable logistic regression model was used to identify independent risk factors for PSNU. RESULTS: Three risk factors were found to be independently significant predictors of PSNU: age at the time of surgery, dominant hand injury, and previous surgery on the affected scaphoid. With every decade of a patient's life, dominant hand injury, and previous scaphoid surgery, the odds of union are reduced by 1.72 times, 7.35 times, and 4.24 times, respectively. CONCLUSION: We identified three independent risk factors for PSNU: age at SNU surgery, dominant hand injury, and previous surgery on the affected scaphoid. The findings of this study are significant and may contribute to shared decision-making and prognostication between the patient, surgeon, and affiliated members of their care team.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".