A Randomized Trial of Real-Time Geriatric Assessment Reporting in Nonelectively Hospitalized Older Adults with Cancer
Bibliographic record
Abstract
BACKGROUND: Hospitalized older adults have significant geriatric deficits that may lead to poor outcomes. We conducted a randomized trial to investigate the effectiveness of providing clinicians with a real-time geriatric assessment (GA) report in nonelectively hospitalized older patients with cancer. SUBJECTS, MATERIALS, AND METHODS: We developed a web-based software platform for administering a modified GA (Cancer 2005;104:1998-2005) to older (>70 years) nonelectively hospitalized patients with pathologically confirmed malignancy. Patients were randomized to have their GA report provided to their treating clinicians (Intervention arm) or not provided (Control arm). RESULTS: Our study included 135 patients, median age 76 years, 52% female, 75% white, 21% black, 79% greater than high school education, 59% married, and 17% living alone. All patients had at least one GA-identified deficit, including physical function deficits (90%), cognitive impairment (22%), >5 comorbidities (28%), polypharmacy (>9 medications; 38%), weight loss ≥10% in the past 6 months (40%), anxiety (32%), or depression (30%). There was no difference between the Intervention (6%) and Control arms (9%) in the proportion of patients who were referred by their clinical team for an intervention to address a deficit (p = .53). CONCLUSION: Many older nonelectively hospitalized patients with cancer have geriatric deficits that are amenable to evidence-based interventions. Real-time GA reports provided to the care team prior to discharge did not influence provider referral for such interventions. There is a need for systems-level interventions to address deficits in this vulnerable patient population. IMPLICATIONS FOR PRACTICE: Geriatric deficits are common in hospitalized older adults with cancer and lead to poor outcomes. Addressing modifiable deficits represents an appealing way to improve outcomes. Widespread geriatrician consultation is impractical owing to resource and personnel constraints. This work tested whether prompt delivery of a mostly self-administered, web-based geriatric assessment report to clinicians improved referral rates for evidence-informed interventions. It confirmed frequent geriatric deficits and high readmission rates in this population but found that real-time geriatric assessment reporting did not influence provider referral for evidence-informed interventions on geriatric assessment identified deficits. These findings highlight the need for systems-level intervention to improve outcomes in this vulnerable patient population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".