Patient- versus clinician-reported symptoms in the POLARIX study.
Bibliographic record
Abstract
12100 Background: The safety profiles of novel agents are mainly based on clinician-reported adverse events (AEs) from clinical trials. Patient-reported outcomes (PROs) may better represent the treatment burden experienced by patients (pts) compared with clinician-reported AEs. Using data from POLARIX, a double-blind, placebo-controlled, randomized Phase 3 international study (NCT03274492), we previously presented PRO and clinician-reported data showing similar rates of neuropathy (Trněný et al. 2022). Here, we evaluate the reporting of other common symptoms using PRO and clinician-reported data in POLARIX. Methods: POLARIX methods were previously described (Tilly et al. 2022); this analysis included all pts with PRO data. PRO and clinician-reported data described the incidence and severity of fatigue, constipation, diarrhea, nausea, and vomiting. Clinician-reported severity grading was based on the NCI Common Terminology Criteria for Adverse Events (NCI-CTCAE) v4.0. PROs were collected using the European Organisation Research and Treatment of Cancer Quality of Life-Core 30 questionnaire (EORTC QLQ-C30), which was administered to pts at clinic visits on Day 1 of Cycles 1 (baseline), 2, 3, and 5, and end of treatment (EOT). PRO severity scores included ‘A little’, ‘Quite a bit’, and ‘Very much’. Results: Overall, 825 pts were evaluable. From baseline to EOT, PROs showed a higher incidence of symptoms compared with clinicians for fatigue (98% vs 27%), constipation (68% vs 29%), diarrhea (56% vs 26%), nausea (58% vs 40%), and vomiting (24% vs 15%). PRO severity scores of ‘Quite a bit’ or ‘Very much’ were reported in 33% of pts for fatigue, 29% for constipation, 17% for diarrhea, 19% for nausea, and 7% for vomiting. Clinicians reported Grade ≥2 symptoms in 11% of pts for fatigue, 11% for constipation, 11% for diarrhea, 14% for nausea, and 6% for vomiting. Conclusions: In POLARIX, pts reported a higher incidence and severity of symptoms compared with clinicians. Although distinct scales were used, the differences in symptom rates reported by pts and clinicians were clinically meaningful. These data may have implications for symptom management, including physician evaluation and communication of symptom expectations for pts. Reporting of symptoms by PROs should be incorporated into clinical trials as an adjunct to standard AE reporting to better characterize the patient experience. Clinical trial information: NCT03274492 . [Table: see text]
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".