MétaCan
Menu
Back to cohort

Development of an evidence‐based protocol for care of pilonidal sinus wounds healing by secondary intent using a modified reactive Delphi procedure. Part one: the literature review<sup>*</sup>

2011· review· en· W1970806743 on OpenAlexaff
Connie Harris, Samantha Holloway

Bibliographic record

VenueInternational Wound Journal · 2011
Typereview
Languageen
FieldMedicine
TopicAnorectal Disease Treatments and Outcomes
Canadian institutionsRegional Municipality of Waterloo
Fundersnot available
KeywordsMedicineWound careWound healingGuidelineSurgeryNegative-pressure wound therapyProtocol (science)Sinus (botany)Intensive care medicineAlternative medicinePathology

Abstract

fetched live from OpenAlex

This article is in two parts. The overall aim of this section was to review the literature in relation to pilonidal sinus wounds (PSW) healing by secondary intent for a Master's of Science in Wound Healing and Tissue Repair thesis. The purpose of the literature review was to determine if an evidence-based guideline or consensus document existed for the care of these wounds, and if not, to determine the topics from which to develop items for the first round of a modified reactive Delphi questionnaire. Part two will describe the iterative process, the analysis and the results. The review found no best practice guidelines concerning PSW, and only one clinical pathway. Seventeen areas of interest were identified that may contribute to optimal healing conditions or to delayed healing. These included microbiology of infected PSW, signs and symptoms of localised or deeper (spreading) chronic wound infection, swab for c&s, role of topical antiseptics or antimicrobials, systemic antibiotics, local wound interventions, optimal positioning, wound cleansing, principles of moist wound healing/dressing selection, topical negative pressure (TNP) therapy, peri-wound skin decontamination and depilation, pain control, physical activities, optimal nutrition and patient education.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.811
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
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 head0.163
GPT teacher head0.427
Teacher spread0.264 · 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 designOther design
Domainnot available
GenreReview

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

Citations16
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueInternational Wound JournalSame topicAnorectal Disease Treatments and OutcomesFrench-language works237,207