Excision With Interpositional Nerve Grafting
Bibliographic record
Abstract
INTRODUCTION: "Morton neuroma" is a common cause of forefoot pain with numbness frequently occurring in the distribution of the third common digital nerve. After the failure of nonoperative measures, decompression with excision of the neuroma is common practice. Residual numbness and recurrent pain has been reported as a consequence of this treatment option. This study describes excision of the neuroma with interpositional nerve grafting as a treatment option for Morton neuroma. This proposed technique has the benefit of reducing pain, reducing recurrent secondary neuromas and restoring postexcision sensory deficits. METHODS: A retrospective chart review of patients who underwent elective primary excision of a Morton neuroma with interpositional nerve grafting was undertaken. Patient demographics, surgical technique, and clinical outcomes, such as pain, neuroma recurrence, 2-point discrimination, numbness, and weight-bearing status at minimum of 1 year postoperation, are reported. RESULTS: Eight patients (9 neuromas) underwent excision of the Morton neuroma with interpositional nerve grafting after failing nonoperative measures. At final follow-up, all patients had improvement of pain and there were no neuroma recurrences. Sensation to the grafted hemi-toe returned in all but 1 case. All patients returned to full weight-bearing status. Although no major complications were reported, wound dehiscence secondary to a hematoma occurred in 1 case. CONCLUSIONS: Excision and interpositional nerve grafting is an effective treatment for Morton neuroma as it alleviates pain, numbness and restores sensation with minimal morbidity and complications.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".