Canadian Association for Enterostomal Therapy Evidence-Based Recommendations for Conservative Sharp Wound Debridement
Bibliographic record
Abstract
The Canadian Association for Enterostomal Therapy Conservative sharp wound debridement evidence-based recommendations are a result of the decision and commitment to advance clinical nursing practice to improve the provision of care to patients via development of an open source guide. The recommendations were developed by a volunteer group of ET nurses who work in clinical practice, policy development, consultation, and education in wound care. The document was developed over the course of 2 years (2009-2011); it is a distillation of existing literature, guidelines, and expert opinion. The development and dissemination of the recommendations were sponsored by the Canadian Association for Enterostomal Therapy. These recommendations should be considered in the context of the organization or care setting as well as available resources and supports. Resources and supports take the form of access to emergency care, physicians and allied health care professionals, education, administrative support, funding, supplies, equipment, and policy. The recommendations should also be applied with consideration of the evolving evidence that will further define practices in Conservative Sharp Wound Debridement. Refer to the supplemental digital content associated with this article at (supplemental digital content 1, http://links.lww.com/JWOCN/A16) for the complete document.
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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 | 0.015 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".