Geriatric Assessment of Older Adults With Cancer During Unplanned Hospitalizations: An Opportunity in Disguise
Bibliographic record
Abstract
BACKGROUND: Geriatric assessment (GA) is an important tool for management of older cancer patients; however, GA research has been performed primarily in the outpatient setting. The primary objective of this study was to determine feasibility of GA during an unplanned hospital stay. Secondary objectives were to describe deficits found with GA, to assess whether clinicians recognized and addressed deficits, and to determine 30-day readmission rates. MATERIALS AND METHODS: The study was designed as an extension of an existing registry, "Carolina Senior: Registry for Older Patients." Inclusion criteria were age 70 and older and biopsy-proven solid tumor, myeloma, or lymphoma. Patients had to complete the GA within 7 days of nonelective admission to University of North Carolina Hospital. RESULTS: A total of 142 patients were approached, and 90 (63%) consented to participation. All sections of GA had at least an 83% completion rate. Overall, 53% of patients reported problems with physical function, 63% had deficits in instrumental activities of daily living, 34% reported falls, 12% reported depression, 31% had ≥10% weight loss, and 12% had abnormalities in cognition. Physician documentation of each deficit ranged from 20% to 46%. Rates of referrals to allied health professionals were not significantly different between patients with and without deficits. The 30-day readmission rate was 29%. CONCLUSION: GA was feasible in this population. Hospitalized older cancer patients have high levels of functional and psychosocial deficits; however, clinician recognition and management of deficits were poor. The use of GA instruments to guide referrals to appropriate services is a way to potentially improve outcomes in this vulnerable population. IMPLICATIONS FOR PRACTICE: Geriatric assessment (GA) is an important tool in the management of older cancer patients; however, its primary clinical use has been in the outpatient setting. During an unplanned hospitalization, patients are extremely frail and are most likely to benefit from GA. This study demonstrates that hospitalized older adults with cancer have high levels of functional deficits on GA. These deficits are under-recognized and poorly managed by hospital-based clinicians in a tertiary care setting. Incorporation of GA measures during a hospital stay is a way to improve outcomes in this population.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".