Is patient self-reporting more accurate than clinician reporting of symptoms for predicting survival in patients with cancer? Meta-analysis of 30 closed EORTC randomized controlled trials
Bibliographic record
Abstract
9597 Background: This study investigated whether patient self-reporting of symptoms improved prediction of survival as compared to clinician reporting or whether it provided an additive value when taken together with clinician assessment of the same symptoms. Methods: Patients with advanced cancer from 30 European Organisation for Research and Treatment of Cancer (EORTC) Randomized Controlled Trials were included in this retrospective pooled analysis. Clinician [Common Toxicity Criteria (CTC)] and patient (EORTC QLQ-C30) symptom assessment were reported at entry into the study. Data were obtained for six symptoms: pain, fatigue, vomiting, nausea, diarrhea and constipation. The prognostic accuracy for survival was assessed by modeling the contrast in reporting using the Harrell's discrimination c-index (c). Results: Data were available from patient and clinician assessment for pain [number of trials (t) =8, number of patients (n) =1214], fatigue [t=5, n=1237], vomiting [t=5, n=824], nausea [t=6, n=1393], diarrhea [t=6, n=815] and constipation [t=4, n=751]. Fatigue (c=0.59 vs 0.55, p<.01) and constipation (c=0.57 vs 0.52, p=0.03) as reported by patients (vs clinicians) were significantly higher in predicting survival. Patient reported pain (c=0.59 vs 0.58, p=0.17), nausea (c=0.54 vs 0.52, p=0.51), vomiting (c=0.55 vs 0.52, p=0.21) and diarrhea (c=0.51 vs 0.52, p=0.49) did not predict survival any more accurately than clinician assessment. Patient and clinician assessment combined (vs clinicians alone) improved the prognostic accuracy for fatigue (c=0.61 vs 0.55, p=0.01), pain (c=0.60 vs 0.58, p<0.01), nausea (c=0.54 vs 0.52, p=0.04), vomiting (c=0.56 vs 0.52, p=0.04) and constipation (c=0.5 vs 0.52, p=0.01), but not for diarrhea (c=0.52 vs 0.52, p=0.44). Conclusions: Our results suggest that patients’ ratings of their own fatigue and constipation have more prognostic value than clinicians’ ratings of these symptoms. Further, the prognostic value of clinicians's ratings can be improved by combining them with patients’ assessments for the symptoms pain, fatigue, constipation, nausea and vomiting. 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 | 0.077 | 0.110 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.065 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".