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>
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.254 | 0.173 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".