The influence of patient-reported adverse events on Health Utility Score (HUS) and Health-Related Quality of Life (HRQoL) in small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) patients.
Bibliographic record
Abstract
185 Background: The Patient Reported Outcome version of Common-Terminology-Criteria-for-Adverse-Events (PRO-CTCAE) and Edmonton Symptom Assessment System (ESAS) are validated tools that measure toxicities and symptoms in cancer patients. We compared the extent in which the presence and severity of toxicities and symptoms affected HUS in SCLC and NSCLC patients. Methods: Adult SCLC and NSCLC patients were recruited from the Princess Margaret Cancer Centre and surveyed cross-sectionally for clinico-demographic variables, EQ5D-5L, PRO-CTCAE and ESAS. HUS were estimated using EQ5D-5L. PRO-CTCAE toxicities include diarrhea, constipation, decreased appetite, nausea, vomiting, fatigue, neuropathy and rash. ESAS symptoms include pain, tiredness, drowsiness, appetite loss, nausea, shortness of breath, depression, anxiety and lack of well-being. These were combined to show frequency and average severity of toxicities/symptoms per patient. Univariable and multivariable linear regression analyses identified toxicity/symptom influencing HUS. Results: Of 75 SCLC and 150 NSCLC, 52% were male with median age of 65 years. The mean HUS was 0.76 (SCLC = 0.69; NSCLC = 0.79; p = 0.001). Compared to NSCLC, SCLC patients had a significantly higher number of toxicities (3.14 versus 1.33 using PRO-CTCAE, p < 0.0001); symptoms (6.75 vs 5.45 using ESAS, p = 0.0003) and severity of toxicities/symptoms (PRO-CTCAE 0-4: 0.95 versus 0.35, p < 0.0001; ESAS 0-10: 3.37 versus 2.04. p < 0.0001). There were significant correlations between the average severity of toxicities/symptoms and HUS (p < 0.0001) adjusted for age, histology, smoking pack years and performance status. For each increase in the average severity of toxicities, there was a corresponding mean drop of 0.03 in the HUS; for every increase in the average severity of symptoms, the drop was 0.04. These relationships were similar for both SCLC and NSCLC patients. Conclusions: Patient reported toxicities and symptoms have a significant impact on HUS in both SCLC and NSCLC patients. Early and aggressive management of such adverse events may be necessary to improve patients’ HRQoL.
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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 | 0.002 | 0.009 |
| 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 | 0.000 |
| 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".