Symptom practice guide for telephone assessment of patients with cancer treatment-related cardiotoxic dyspnea: Adaptation and evaluation of acceptability
Bibliographic record
Abstract
BACKGROUND: Patients with cancer treatment-related cardiotoxicity, which may manifest as heart failure (HF), can present with dyspnea. Nurses frequently assess, triage and offer self-care strategies to patients experiencing dyspnea in both the cardiology and oncology settings. However, there are no known tools available for nurses to manage patients in the setting of cancer treatment-related cardiotoxicity. The objective of this study was to adapt and evaluate the acceptability of an evidence-informed symptom practice guide (SPG) for use by nurses over the telephone for the assessment, triage, and management of patients experiencing dyspnea due to cancer treatment-related cardiotoxicity. METHODS: The CAN-IMPLEMENT© methodology guided this descriptive study. A systematic search was conducted in four databases to identify cardio-oncology and HF guidelines and systematic reviews. Screening was conducted by two reviewers, with data extracted into a recommendation matrix from eligible guidelines and systematic reviews on: assessment criteria, medications, and/or self-care strategies to manage dyspnea. Healthcare professionals with an expertise in oncology and/or cardiology were recruited using purposeful and snowball sampling. Evaluation of acceptability of the adapted SPG was gathered through semi-structured interviews and a survey with open- and closed-ended questions. Quantitative findings and participant feedback from the interviews and the open-ended survey questions were analyzed descriptively. RESULTS: Of 490 citations, seven HF guidelines were identified. Evidence from these guidelines was added to the original SPG. Eleven healthcare professionals completed the interview and acceptability survey. The adapted SPG was iteratively revised three times during the interviews. The original SPG was adaptable, and participants indicated the adapted SPG was comprehensive, easy to follow, and would be useful in clinical practice. CONCLUSIONS: This study highlights the lack of knowledge tools and available clinical practice guidelines to guide healthcare professionals to assess, triage and/or offer self-care strategies to patients with cancer treatment-related cardiotoxic dyspnea. Moreover, most nurses require assistance to differentiate among the various causes of dyspnea from oncology treatment in order to triage severity appropriately. Further research should focus on evaluating the validity of the adapted SPG in clinical practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".