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Record W3027855936 · doi:10.1177/0300060520925372

Comparison of the modified direct closure method and skin grafting for wounds at the anterolateral thigh flap donor site

2020· article· en· W3027855936 on OpenAlexaboutno aff
Song Gu, Yuxuan Zhang, Yinjun Huang, Huichao Fu, Guheng Wang, Renguo Xie

Bibliographic record

VenueJournal of International Medical Research · 2020
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSurgeryCosmesisSkin graftingThighWound closureGraftingWound healing

Abstract

fetched live from OpenAlex

OBJECTIVE: This study was performed to compare the modified direct closure method and traditional skin grafting for wounds at the anterolateral thigh (ALT) flap donor site. METHODS: Among 29 consecutive patients with wounds at the ALT flap donor site, 14 underwent the modified direct closure method (MDC group) and 15 underwent traditional skin grafting (SG group). The operative time, follow-up time, complications, Vancouver Scar Scale (VSS) score, and Scar Cosmesis Assessment and Rating (SCAR) score of the two groups were statistically analyzed. RESULTS: The mean follow-up times in the MDC and SG group were 16.1 and 16.7 months, respectively. Two patients showed partial skin necrosis after skin grafting, but the remaining patients' wounds healed uneventfully. The operative time in the MDC group was an average of about 64 minutes shorter than that in the SG group. The average VSS and SCAR scores in the MDC group were 2.1 and 3.0 points lower, respectively, than those in the SG group. CONCLUSIONS: Compared with traditional skin grafting, the modified direct closure method is more efficient for repair of wounds at the ALT flap donor site because of its shorter operative time, better postoperative appearance of the donor site, and higher patient satisfaction.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.184
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
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 integrity0.0000.001
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 head0.148
GPT teacher head0.509
Teacher spread0.361 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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