Identifying tools for capturing the overall experience of dyspnea in a lung cancer population: The patient’s perspective.
Bibliographic record
Abstract
73 Background: Efficient methods of tracking dyspnea can improve quality of care. We asked lung cancer patients to assess five validated patient-reported outcome (PRO) tools and determine whether these tools captured different domains of their dyspnea experience. Methods: This cross-sectional study of adult lung cancer outpatients of all stages utilized touch screen tablets to administer five dyspnea tools (Borg severity (B), Reduced Cancer Dyspnea (R), Breathlessness intensity (I), breathlessness distress (D), and MRC breathlessness (M) scales) that focused on the severity, experience, intensity, extent, and functional impairment of dyspnea, respectively. Patients were then asked whether each tool captured their dyspnea experience. Results: Of 226 lung cancer patients, 120 reported some level of dyspnea, and their responses were analyzed. Median age (range) was 67 (30-97) years; 53% were males; 37% were stage I-II; 56%, were stage III-IV. All the tools except B were completed by over 90% of patients (R 95%, I 93%, D 91%, M 91%). 71% of patients thought that M captured functional impairment well, while 58-62% of patients thought that R, S, A captured experience, intensity and distress well. B had the lowest completion rate (83%) and the lowest patient perception that is captured severity of dyspnea well (49%). Qualitative analysis suggests that most dyspnea is activity-related in this population, which would be consistent with patients favoring M (functional assessment) over B (dyspnea at present in clinic). I+D+M takes under 5 minutes to complete, whilst R takes 5+ minutes alone to complete. Conclusions: In a sample of cancer patients with high prevalence of dyspnea, patients felt four of five tools were useful in capturing various domains of their dyspnea experience. The majority of patients felt that the questions were relevant to their circumstances. I, D, M are appropriate screening tools, whilst R may be useful under specific circumstances. Our next step is the application and evaluation of self management tools in the dyspnea setting using these four tools.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".