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The operative treatment of pressure wounds: a 10‐year experience in flap selection

2010· article· en· W2028922768 on OpenAlexaff
Romy Ahluwalia, Daniel Martín Muñoz, James Mahoney

Bibliographic record

VenueInternational Wound Journal · 2010
Typearticle
Languageen
FieldHealth Professions
TopicPressure Ulcer Prevention and Management
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicinePressure soresSurgeryPosterior compartment of thighIschiumComplicationIschial tuberosityThighDemographicsNegative-pressure wound therapy

Abstract

fetched live from OpenAlex

This study sought to both assist in the selection of flaps for ischial pressure wound re-construction and to evaluate the overall complication rates associated with re-construction. A retrospective medical record review was conducted for 78 patients following the surgical re-construction of an ischial pressure sore. Records were reviewed for demographics, location of sores, methods of re-construction and flap selection, as well as any complications and recurrences. Seventy-two wounds were re-constructed with an average of 1.4 flaps used per wound. An ischial flap complication rate of 16% was observed in flap follow up, with a recurrence rate of 7% recorded. The vast majority of complications went on to heal with 15% of patients requiring a second re-construction. Our relatively large sample of ischial flaps allowed for a close comparison with previously published work. Both flap selection and site of reconstruction significantly affected the success rates for pressure sore coverage. The overall complication rates by flap and re-constructive site in this review are lower than previously published reports. Our experience with ischial re-construction was extensive enough to suggest a posterior medial thigh fasciocutaneous flap combined with a biceps femoris muscle flap as a first choice in ischial pressure wound re-construction.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.840
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0050.000

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 head0.043
GPT teacher head0.455
Teacher spread0.412 · 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 designNot applicable
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

Citations23
Published2010
Admission routes1
Has abstractyes

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