054 Transforming Evidence From Multiple Guidelines Into User Friendly Clinical Practice Tools For Remote Cancer Treatment-Related Symptom Management: The Costars Project
Bibliographic record
Abstract
Objectives To transform evidence from guidelines for clinical practice tools for remote management of patients experiencing symptoms related to cancer treatment. Clinical practice protocols are defined as user-friendly knowledge translation tools to support patient care. These tools narrow the know-do gap by presenting the best available evidence from guidelines while using a format that is sensitive to how nurses think and what nurses do. Methods Mixed methods descriptive study guided by CAN-IMPLEMENT©. The process involved: a) conducting a systematic search for guidelines; b) developing symptom-specific protocols using evidence from quality appraised clinical practice guidelines; c) reaching consensus on the clinical practice protocol template, and d) validating the clinical practice protocols. Results Clinical practice protocols were developed and validated for 13 symptoms using 42 clinical practice guidelines with a median of 3 guidelines per protocol (range 1 for bleeding to 7 vomiting). For the first two protocols, source guideline AGREE rigour subscale ratings ranged from 8% to 86% (median 60.1; diarrhoea; 40.5 fever). The protocols were developed using guidelines, symptom severity questions included the Edmonton Symptom Assessment System, and iterative feedback from practicing nurses. Usability testing revealed: high readability, just the right amount of information, and appropriate terms. Access to protocols needs to be tailored to individual practices (e.g. electronic application, access to paper-based versions). Nurses requested training and support to implement them. Discussion These tools, created from guidelines, transform evidence into user-friendly protocols for use by nurses when guiding patients at home to better manage their cancer treatment-related symptoms.
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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 | 0.231 | 0.246 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".