Algorithmic approaches to ostomy management: An integrative review
Bibliographic record
Abstract
OBJECTIVE: The aim of this review is to describe approaches to ostomy management utilizing algorithmic approaches found within the literature. DESIGN: An integrative review approach was used based on a modified Cooper's five-stage research review framework. DATA SOURCES: Systematic searches occurred using the CINAHL and MEDLINE databases searching for peer-reviewed, English publications. REVIEW METHODS: There were 640 articles identified through the review process, 608 of which were excluded based on title and abstract review. The remaining 12 articles were assessed in full text after which two studies were removed as duplicates and six studies were excluded based on inclusion/exclusion criteria. Four studies were included in this synthesis. Studies were critically analysed using a critical appraisal tool developed for both qualitative and quantitative study assessments. RESULTS: Utilizing inductive content analysis, included literature was presented within two categories: validation of ostomy algorithms and implementation of ostomy algorithms in practice. Four themes emerged from these categories including the following: algorithm validation, identifying underlying causes, focus on accessories and large-scale implementation. CONCLUSION: No currently available validated algorithms published in full were found during this literature review. Current literature demonstrates the potential benefit for ostomy management algorithms to standardize and improve ostomy patient care. IMPACT: This study sought to determine the availability and supporting research of ostomy management algorithms which may assist in standardizing and improving ostomy care. This review has demonstrated a lack of available ostomy management algorithms. Given the potential benefit of ostomy algorithms identified within the literature, further studies should be completed to develop, validate and test new ostomy management algorithms.
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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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".