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Record W4390116112 · doi:10.1097/gox.0000000000005498

Split-thickness Plantar Skin Graft for Foot Syndactyly

2023· article· en· W4390116112 on OpenAlexaboutno aff
Yoko Tomioka, Mutsumi Okazaki, Hitomi Matsutani

Bibliographic record

VenuePlastic & Reconstructive Surgery Global Open · 2023
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsnot available
FundersUniversity of Tokyo
KeywordsSyndactylyMedicineSurgeryFoot (prosody)Soft tissue

Abstract

fetched live from OpenAlex

For the treatment of syndactyly, the submalleolar or inguinal area is the common donor site for skin grafts. However, high skin tension of the submalleolar area could potentially delay wound healing or cause scarring. Skin grafts from the inguinal area cause pigmentation. In this study, we have harvested split-thickness skin grafts from the plantar area to treat syndactyly and evaluated the healing course and aesthetic outcome. We analyzed 13 recipient and nine donor sites in eight patients, aged 13-68 months (average 25 months), with syndactyly of the foot. The minimum follow-up was 14 months, and average follow-up period was 22.3 months. Aesthetic outcomes including color and texture match, wound healing of donor site using Vancouver Scar Scale, and complications of both sites were assessed in all patients. At the recipient sites, the graft survived well, and the lack of pigmentation of the graft led to good color match. At the donor sites, hypertrophic scar and high scar scale were seen around postoperative month 3, but were momentary, as all donor sites matured to a flat and soft scar. Morbidity of split-thickness skin graft from the plantar region is limited. It causes minimum scarring of the nonexposed area. Moreover, because it does not cause pigmentation, the split-thickness skin graft technique is a reasonable option for the treatment of syndactyly.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.052
GPT teacher head0.334
Teacher spread0.282 · 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 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
Published2023
Admission routes1
Has abstractyes

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