Use of patient-reported outcomes (PROs) to predict treatment outcomes in patients with advanced cancer.
Bibliographic record
Abstract
186 Background: PROs assessing quality of life (QOL) and physical symptoms often correlate with clinical outcomes in patients (pts) with cancer. Yet, data are lacking about the use of PROs to predict treatment response. We evaluated associations of baseline PROs with treatment response, healthcare use, and survival among pts with advanced gastrointestinal cancer. Methods: We prospectively enrolled pts with metastatic gastrointestinal cancer prior to initiating chemotherapy at Massachusetts General Hospital. At baseline (start of treatment), pts reported their QOL (Functional Assessment of Cancer Therapy General [FACT-G], subscales assess QOL across 4 domains: functional, physical, emotional, social well-being) and symptom burden (Edmonton Symptom Assessment System [ESAS]). Higher scores on FACT-G indicate better QOL, while higher scores on ESAS represent a greater symptom burden. We used regression models to examine associations of baseline PRO scores with treatment response (clinical benefit [CB] or progressive disease [PD] at the time of first scan based on clinical documentation), healthcare use (unplanned hospital admissions), and survival. Results: From 5/2019-3/2020, we enrolled 112 of 131 (85.5% enrollment) consecutive pts (median age = 62.8, 61.6% male, 45.5% pancreatobiliary cancer). For treatment response, 64.3% had CB and 35.7% had PD. Higher ESAS-physical (B = 1.04, p = .027) and lower FACT-G functional (B = 0.92, p = .038) scores at baseline were significant predictors of PD. On the specific ESAS items, pts who experienced PD were more likely to report moderate/severe poor well-being (57.9% vs 29.7%; p = .001), pain (44.7% vs 25.0%; p < .050), drowsiness (42.1% vs 20.3%; p = .024), and diarrhea (23.7% vs 4.7%; p = .008) at baseline. Lower FACT-G total (HR = 0.96, p = .003), FACT-G physical (HR = 0.89, p < .001), FACT-G functional (HR = 0.87, p < .001), and higher ESAS-physical (HR = 1.03, p = .028) scores at baseline were significantly associated with greater risk of hospital admission. Lower FACT-G total (HR = 0.96, p = .009), FACT-G emotional (HR = 0.87, p = .014), as well as higher ESAS-total (HR = 1.03, p = .018) and ESAS-physical (HR = 1.03, p = .040) scores at baseline were significantly associated with greater risk of death. Conclusions: We found that baseline PROs predict treatment response in pts with advanced cancer, namely physical symptoms and functional QOL, in addition to healthcare use and survival outcomes. These findings further support the use of PROs to predict important clinical outcomes, including the novel finding of treatment response.
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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 | 0.003 | 0.008 |
| 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".