Improving care of older adults with cancer: A randomized trial.
Bibliographic record
Abstract
11002 Background: Undertreated symptoms are common among older adults with cancer. Previously, a lay health worker (LHW)-led proactive symptom assessment intervention was associated with reduced symptoms in one community clinic. Yet, the effect on acute care use, total costs, and end-of-life (EOL) care at scale remains unknown. Methods: Adults ages 75 years or older who were Medicare Advantage beneficiaries with newly diagnosed cancer were eligible to participate in this randomized trial across 43 clinics in Southern California and Arizona. Participants were randomized 1:1 into a control group (usual care alone) or an intervention group (usual care and LHW-led proactive, telephone-based weekly symptom assessments for 12 months using the validated Edmonton Symptom Assessment System) with a planned enrollment of at least 200 in both groups. The LHW reviewed assessments with a physician assistant who conducted follow-up for symptoms that changed by 2 points from a prior assessment or were rated 4 or greater. We used generalized regression models to compare acute care use and total costs (obtained from payer claims data) for 12-months follow-up or death, whichever was first, offset for length of follow-up, and, among those who died, compared EOL acute care use, costs, and acute care facility deaths. Results: 416 patients participated (216 control; 200 intervention) with median age of 82 years (range 75-99); 205 (49.28%) were Hispanic or Latino, 10 (2.4%) African American or Black, 12 (2.88%) Asian, 2 (0.48%) Native Hawaiian, 1 (0.24%) Pacific Islander, 180 (43.3%) Non-Hispanic White, 6 (1%) other; 219 (52.6%) were male; 118 (28.3%) had gastrointestinal, 92 (22%) had genitourinary, 62 (14.9%) had breast, and 48 (11.5%) had thoracic cancer; 171 (41%) had stage 4 disease. The intervention group had 53% lower odds of emergency department use (OR: 0.47, 95% CI 0.37-0.62) and 68% lower odds of hospital use (OR: 0.32, 95% CI 0.20-0.51) than the control group. Among deceased participants (71 (32.9%) control; 71 (35.5%) intervention), the intervention group had 68% lower odds of acute care (OR: 0.32, 95% CI 0.12-0.88), lower total costs of care by $12,000 USD per participant (p = 0.01), and 75% lower odds of an acute care facility death (OR 0.25; 95% CI 0.08-0.77). Conclusions: This proactive symptom assessment intervention may be one sustainable, scalable, efficient, and effective approach to improve care for older adults with cancer. Clinical trial information: NCT04463992 .
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".