The predictive accuracy of survival between patient-reported versus clinician-reported pain in a cohort of 1,214 patients with metastatic cancer
Bibliographic record
Abstract
9607 Background: Accurate assessment of pain involves cooperation between clinician and patient. However, in patients with metastatic disease agreement between clinician and patient ratings is known to be poor. The objectives of this meta-analysis are to investigate the degree of agreement between clinician- versus patient- reported cancer pain at entry in a cohort of patients with metastatic cancer and whether their ratings were associated with a difference in survival. Methods: Eight European Organisation for Research and Treatment of Cancer (EORTC) Randomized Controlled Trials (RCT), across different cancer sites, were eligible for this study. Pain was scored at baseline by the clinician [Common Toxicity Criteria (CTC)] and the patient (EORTC QLQ-C30). The Wilcoxon rank sign test was applied to investigate scoring differences between patient- versus clinician- reported pain and logistic regression to model whether clinical parameters, i.e., performance status, gender, age or cancer site, affected scoring differences. The model accuracy of both scorings was investigated with the Harrell's discrimination c-index (c) after correction for the clinical parameters. Results: 1214 patients provided valid patient- and clinician- reported pain data at entry. Cancer pain was specified as bone metastasis by 643 (53%) patients and not specified otherwise. The overall mean pain as scored by the clinician was 2.25 (standard deviation (SD) 1.1) and by the patient was 2.28 (SD=0.95) on a 1 to 4 scale. Scoring differences were found to be statistically significant for colorectal (p<.01), lung (p<.01), prostate (p<.01), and breast (p=0.03, but not for pancreatic cancer (p=0.49). Clinical parameters did not significantly affect the scoring differences. Pain as reported by patients (vs clinicians) showed similar predictive accuracy (c =0.62 vs 0.61, p=0.59). Conclusions: Our results provide further evidence that significant differences exist in pain reporting between clinicians and patients. Such results provide a rationale to include patient self reported pain assessment in future cancer RCTs to better assess disease status and survival prognosis. No significant financial relationships to disclose.
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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 | 0.031 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.017 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".