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Record W4401439423 · doi:10.25270/wnds/23136

Use of Collagen Powder in Secondary Intention Healing After Mohs Surgery or Excisional Surgery: A Retrospective Study

2024· article· en· W4401439423 on OpenAlexaboutno aff
L. G. Kendall, D. Hurst, John Monahan, Sidney P Smith

Bibliographic record

VenueWOUNDS A Compendium of Clinical Research and Practice · 2024
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMohs surgeryRetrospective cohort studySurgeryWound healingGeneral surgery

Abstract

fetched live from OpenAlex

BACKGROUND: Little is known about the usefulness of collagen powder in secondary intention healing in patients undergoing cutaneous surgery. OBJECTIVE: To investigate the clinical outcomes associated with application of collagen powder in cutaneous surgery and patients' perceptions of the procedure. METHODS: A retrospective chart review of 266 patients who underwent Mohs surgery or excisional surgery at a single institution between January 2020 and January 2022, and who had secondary intention healing of wounds assisted by powdered collagen was conducted. Personal interviews were conducted with 63 of those patients (23.7%). Tumor characteristics, estimated healing times, and patient satisfaction were scored. The Vancouver Scar Scale and the Patient and Observer Scar Assessment Scale were used to assess the resulting wound bed. All data underwent statistical analysis. RESULTS: Of 266 granulating wounds with an average defect size of 6.0 cm2, excisional surgery was performed in 143 (54%) and Mohs surgery in 123 (46%). Most procedures (92.1%) were undertaken for nonmelanoma skin cancers. The average healing time was 6.3 weeks. The mean patient score for ease of use and overall impression of collagen application was 8.2 on a scale of 1 to 10, with 10 being most favorable. CONCLUSION: When clinically appropriate, granulation assisted by collagen powder should be considered for augmenting secondary intention healing.

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)0.017
metaresearch head score (Gemma)0.032
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.443
GPT teacher head0.557
Teacher spread0.114 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

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