Treatment of Navicular Stress Fractures With an Algorithmic Approach
Bibliographic record
Abstract
BACKGROUND: Navicular stress fractures are becoming increasingly more common. There is no universal consensus on treatment. We provide an algorithm that we feel will be useful in determining treatment. METHODS: A retrospective study was performed on all patients having operative treatment of navicular stress fractures during a 10-year period. Acute fractures were treated with open reduction internal fixation. Chronic fractures greater than 3 months were treated with open reduction and internal fixation (ORIF) and iliac crest bone grafting. Chronic fractures with evidence of sclerosis, avascular changes, or those who failed previous surgery were treated with ORIF, iliac crest bone grafting, as well as vascular bone grafting. Patients' pain scores were recorded and a return-to-sports scale was used. Radiographic union was compared among the 3 groups using computed tomographic (CT) scans or radiographs. RESULTS: Forty-three patients were identified. Fifteen received ORIF alone, 12 were treated with ORIF and bone graft, and 16 had ORIF with vascularized bone grafting. No difference was found among the median age of the 3 groups. In terms of radiographic healing, 3 patients in the ORIF group received radiographs alone. All other patients had follow-up CT scans. ORIF alone group had 80% union, ORIF with bone graft had 75% union, and ORIF with vascularized bone grafting had 100% union. Return to sports did not show any difference among the 3 groups. CONCLUSION: The algorithm dividing navicular stress fractures into 3 distinct groups with different operative techniques helped us address these difficult cases. Vascularized bone grafting certainly appeared to be beneficial for the more difficult cases. LEVEL OF EVIDENCE: Level IV, case series.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".