RISK FACTORS FOR THE DEVELOPMENT OF PERSISTENT SCAPHOID NONUNION AFTER NONUNION SURGERY
Bibliographic record
Abstract
Between 2014 and 2020, 151 patients with an established scaphoid non-union (SNU) were enrolled in a prospective randomized trial evaluating the effect of Low Intensity Pulsed Ultrasound (the SNAPU trial) on healing in Scaphoid Non-Unions (SNU). All patients had surgery for an established SNU. At trial completion 134 of these patients had complete datasets with known union status. 114 (85%) of these patients went on to union and 20 (15%) went on to recurrent non-union. The main 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 persistent scaphoid non-union (PSNU) in patients who undergo surgery for SNU. Data were extracted from the SNAPU trial database. The inclusion and exclusion criteria of this study were the same as that of SNAPU trial. Nineteen risk factors were determined a priori. Risk factors included (among others): Sex, age, BMI, employment, WCB and smoking status, hand dominance, fracture classification, fracture fixation, specific bone grafting techniques, as well as time from injury to surgery and previous surgery for SNU. A stepwise multivariable logistic regression model was used to identify independent risk factors for PSNU. Only three risk factors were found to be independently significant predictors of persistent scaphoid non-union: age at 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. When operating on an established SNU, we identified three independent risk factors that predicted PSNU. Operations on the dominant hand, on previously operated scaphoids, and older patients showed strong correlation towards recurrent non-unions. 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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".