Pilonidal Sinus Disease: 10 Steps to Optimize Care
Bibliographic record
Abstract
PURPOSE: To present a 10-step approach to the assessment and treatment of pilonidal sinus disease (PSD) and related wounds based on the Harris protocol, expert opinion, and a current literature review. TARGET AUDIENCE: This continuing education activity is intended for physicians and nurses with an interest in skin and wound care. OBJECTIVES: After participating in this educational activity, the participant should be better able to: ABSTRACT: Pilonidal sinus disease (PSD) is a common problem in young adults and particularly in males with a deep natal or intergluteal cleft and coarse body hair. An approach to an individual with PSD includes the assessment of pain, activities of daily living, the pilonidal sinus, and natal cleft. Local wound care includes the management of infection (if present), along with appropriate debridement and moisture management. Treatment is optimized with patient empowerment to manage the wound and periwound environment (cleansing, dressing changes, decontamination, hair removal, minimizing friction). Self-care education includes the recognition of recurrences or infection. Early surgical intervention of these wounds is often necessary for successful outcomes. Pilonidal sinus healing by secondary intention often takes weeks to months; however, the use of the Harris protocol may decrease healing times. A number of new surgical approaches may accelerate healing. Surgical closure by primary intention is often associated with higher recurrence rates. Expert opinion in this article is combined with an evidence-based literature review. The authors have tabulated 10 key steps from the Harris protocol, including a review of the surgical techniques to improve PSD patient outcomes.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".