Development and Evaluation of Evidence-Informed Clinical Nursing Protocols for Remote Assessment, Triage and Support of Cancer Treatment-Induced Symptoms
Bibliographic record
Abstract
The study objective was to develop and evaluate a template for evidence-informed symptom protocols for use by nurses over the telephone for the assessment, triage, and management of patients experiencing cancer treatment-related symptoms. Guided by the CAN-IMPLEMENT© methodology, symptom protocols were developed by, conducting a systematic review of the literature to identify clinical practice guidelines and systematic reviews, appraising their quality, reaching consensus on the protocol template, and evaluating the two symptom protocols for acceptability and usability. After excluding one guideline due to poor overall quality, the symptom protocols were developed using 12 clinical practice guidelines (8 for diarrhea and 4 for fever). AGREE Instrument (Appraisal of Guidelines for Research and Evaluation) rigour domain subscale ratings ranged from 8% to 86% (median 60.1 diarrhea; 40.5 fever). Included guidelines were used to inform the protocols along with the Edmonton Symptom Assessment System questionnaire to assess symptom severity. Acceptability and usability testing of the symptom populated template with 12 practicing oncology nurses revealed high readability (n = 12), just the right amount of information (n = 10), appropriate terms (n = 10), fit with clinical work flow (n = 8), and being self-evident for how to complete (n = 5). Five nurses made suggestions and 11 rated patient self-management strategies the highest for usefulness. This new template for symptom protocols can be populated with symptom-specific evidence that nurses can use when assessing, triaging, documenting, and guiding patients to 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 | 0.475 | 0.547 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".