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Record W2916328725 · doi:10.3138/ptc.2018-42

Mechanical Diagnosis and Therapy and Morton’s Neuroma: A Case Report

2019· article· en· W2916328725 on OpenAlexvenueno aff
Michael David Post

Bibliographic record

VenuePhysiotherapy Canada · 2019
Typearticle
Languageen
FieldMedicine
TopicPeripheral Nerve Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePhysical medicine and rehabilitationPhysical therapyComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.321
Threshold uncertainty score0.824

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.272
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

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