Shampoo after craniotomy: a pilot study.
Bibliographic record
Abstract
OBJECTIVES: The primary goal of this study was to assess the effect of postoperative hair-washing on incision infection and health-related quality of life (HRQOL) in craniotomy patients. The objectives of this study were to 1) determine the effect of postoperative hair-washing on incision infection and HRQOL, 2) provide evidence to support postoperative patient hygienic care, and 3) develop neurosurgical nursing research capacity RESEARCH QUESTION: Does hair-washing 72 hours after craniotomy and before suture or clip removal influence postoperative incision infection and postoperative HRQOL? METHODS: A prospective cohort of 100 adult patients was randomized to hair-washing 72-hours postoperatively (n = 48), or no hair washing until suture or clip removal (n = 52). At five to -10 days postoperatively, sutures or clips were removed, incisions were assessed using the ASEPSIS Scale (n = 85) and participants were administered the SF-12 Health Survey (n = 71). At 30 days postoperatively, incisions (n = 70) were reassessed. RESULTS: No differences were found between hair-washing and no hair-washing groups for ASEPSIS scores at five to 10 days and 30 days, and total SF-12 scores at five to 10 days postoperatively (p > or = 0.05). CONCLUSIONS: Postoperative hair-washing resulted in no increase in incision infection scores or decrease in HRQOL scores when compared to no hair-washing in patients experiencing craniotomy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".