Perceptions of medical status and treatment goal in older adults with advanced cancer.
Bibliographic record
Abstract
e23016 Background: Perceptions of medical status and treatment goal are often used to assess prognostic awareness, but whether these items fully capture patients’ understanding of their prognosis remains unclear. We sought to better understand these measures by investigating their relationship with quality of life (QOL), symptom burden, functional impairment, hospitalizations, and overall survival (OS). Methods: We enrolled patients age ≥70 years within 8 weeks of a diagnosis of incurable gastrointestinal cancer. We surveyed patients to assess perceptions of medical status [terminally ill vs not], treatment goal [curative vs non-curative], QOL (EORTC - Elderly Cancer Patients), symptom burden (Edmonton Symptom Assessment System [ESAS]), and functional impairment (activities of daily living [ADLs]). We used regression models adjusted for age, sex, and education to explore relationships between these items and patients’ QOL, symptom burden, functional impairment, risk of hospitalizations, and OS. Results: Of 132 patients approached, 103 (78.0%) enrolled (mean age 77.62 years, 47.6% female). Half (49.5%) reported a terminally ill medical status and nearly two-thirds (64.0%) reported a non-curative treatment goal, with 42.0% reporting discordant responses to these items. Patient report of a terminally ill status was associated with worse QOL (EORTC illness burden: 53.59 vs 35.26, p = .001), higher symptom burden (ESAS score: 28.15 vs 16.79, p = .002), more functional impairment (number of ADLs: 3.63 vs 5.24, p = .006), greater risk of hospitalizations (HR = 2.41, p = .020), and worse OS (HR = 1.93, p = .010). We found no associations between these outcomes and patient-reported treatment goal. Conclusions: In older adults with advanced cancer, half reported a terminally ill medical status and nearly two-thirds reported a non-curative treatment goal. Patient report of a terminally ill status was associated with worse QOL, symptom burden, functional impairment, risk of hospitalizations, and OS. We did not find associations between these outcomes and patient report of their treatment goal. Our findings suggest that these questions measure different constructs and more nuanced tools for assessing prognostic awareness are needed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".