Mechanical Diagnosis and Therapy and Morton’s Neuroma: A Case Report
Bibliographic record
Abstract
Purpose: Morton’s neuroma (MN) is a neuralgia involving the common plantar digital nerves of the metatarsal region. Evidence-based treatment options for this condition are sparse, and physiotherapy’s usefulness is limited. Client Description: A woman aged 44 years was referred to physiotherapy for left forefoot pain lasting 3 months. The podiatrist diagnosed MN using ultrasonography. Examination found positive squeeze test, painful interphalangeals and metatarsal heads, and painful metatarsophalangeal joint (MPJ) extension. Intervention: Repeated flexion of MPJ digit II relieved the patient’s pain. She was treated six times over 3 months to progress treatment, achieve longer lasting pain relief, and recover function to full pain-free status, including running. Measures and Outcome: The patient’s pain reduced after treatment from a variable 2–7 out of 10 on the Numeric Pain Rating Scale to 0 out of 10. After two sessions, the patient’s Lower Extremity Functional Scale score improved, from 56 out of 80 to 70 out of 80, and by discharge, it was 73 out of 80. At 6-month follow-up, the patient was still running pain-free. Implications: This article describes the rapid and lasting improvement in chronic forefoot pain associated with MN after mechanical diagnosis and therapy assessment and treatment. Finding new, effective, conservative interventions is important for this condition because so few evidence-supported treatments exist. The findings from this case report demonstrate the benefit derived from exercise-based treatment and may indicate a role for physiotherapy in managing MN.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.004 |
| 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".