Summary of Best Practice Recommendations for Management of Enterocutaneous Fistulae From the Canadian Association for Enterostomal Therapy ECF Best Practice Recommendations Panel
Bibliographic record
Abstract
These recommendations are a comprehensive resource summarizing the current literature that supports the care of the person with an enterocutaneous fistula (ECF). They are the result of the decision of the Canadian Association for Enterostomal Therapy to provide an open-source guide to clinicians in the care of the person with ECF. It is intended as a tool for nurses to assist in decision making and priority setting when developing individualized care plans. It is not intended to be a clinical practice guideline but, like its progenitor the Canadian Association for Wound Care: Best Practice Recommendations for Wound Care, it is a distillation of existing research, expert opinion, and case studies intended to enable clinicians to determine their clinical practice based on the best available evidence. It is a living document and as such it is expected that having identified the gaps in knowledge and practice, clinicians will begin the research and publications necessary to fill in these gaps. Contributions to this body of knowledge are essential to an evolving improvement in care for patients living with ECF.
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 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 | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".