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Record W4285798003 · doi:10.2174/18763863-v15-e2205300

The Management of Complex Regional Pain Syndrome-associated Foot Pain using a Poron Insole, a Sponge Upper Padding, and a Post-operative Shoe: A Case Report

2022· article· en· W4285798003 on OpenAlexaff
Min Cheol Chang, Mathieu Boudier‐Revéret, In Sik Park, Yoo Jin Choo

Bibliographic record

VenueThe Open Pain Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicPain Management and Treatment
Canadian institutionsUniversité de Montréal
FundersNational Research Foundation of KoreaNational Research Foundation
KeywordsMedicineComplex regional pain syndromeFoot (prosody)SurgeryPhysical therapy

Abstract

fetched live from OpenAlex

Background: Pain from complex regional pain syndrome (CRPS) is frequently refractory to various treatment methods. Here, we present a case wherein foot pain from CRPS I was managed by applying an insole made from poron (a soft polyurethane foam and highly absorbent material for shock reduction), a sponge upper padding, and a post-operative shoe. Case Presentation: A 47-year-old female patient with CRPS I on her left foot complained of pain for a few months, which was aggravated while standing and walking [numeric rating scale (NRS): 8]. She had a history of a linear fracture in the distal portion of the left 1st metatarsal bone 5 months ago, and the pain from CRPS started 2 months after the fracture. We believed that the aggravated pain during standing and walking was allodynia. We utilized a poron insole, a sponge upper padding, and a post-operative shoe to reduce the pressure and friction loading on her left foot. 1 month after this intervention, the patients’ pain during standing and walking was found to have reduced from NRS 8 to NRS 3. At her 3- and 6-month follow-ups, the degree of pain was sustained at NRS 3. Conclusion: We believe that the reduction of allodynia using materials, which can absorb mechanical pressure and friction of the foot, can help manage pain from CRPS.

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)0.034
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0340.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.057
GPT teacher head0.328
Teacher spread0.271 · 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.

Study designCase report
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

Citations0
Published2022
Admission routes1
Has abstractyes

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