Practical Assessment and Management of Vulnerabilities in Older Patients Receiving Systemic Cancer Therapy: ASCO Guideline Update
Bibliographic record
Abstract
PURPOSE: To update the ASCO guideline (2018) on the practical assessment and management of age-associated vulnerabilities in older patients undergoing systemic cancer therapy. METHODS: An Expert Panel conducted a systematic review to identify relevant randomized clinical trials (RCTs), systematic reviews, and meta-analyses from January 2016 to December 2022. RESULTS: A total of 26 publications met eligibility criteria and form the evidentiary basis for the update. RECOMMENDATIONS: The Expert Panel reiterates its overarching recommendation from the prior guideline that geriatric assessment (GA), including all essential domains, should be used to identify vulnerabilities or impairments that are not routinely captured in oncology assessments for all patients over 65 years old with cancer. Based on recently published RCTs demonstrating significantly improved clinical outcomes, all older adults with cancer (65+ years old) receiving systemic therapy with GA-identified deficits should have GA-guided management (GAM) included in their care plan. GAM includes using GA findings to inform cancer treatment decision-making as well as to address impairments through appropriate interventions, counseling, and/or referrals. A GA should include high priority aging-related domains known to be associated with outcomes in older adults with cancer: physical and cognitive function, emotional health, comorbid conditions, polypharmacy, nutrition, and social support. Clinical adaptation of the GA based on patient population, resources, and time is appropriate.The Panel recommends the Practical Geriatric Assessment as one option for this purpose (https://old-prod.asco.org/sites/new-www.asco.org/files/content-files/practice-patients/documents/2023-PGA-Final.pdf; https://youtu.be/jnaQIjOz2Dw; https://youtu.be/nZXtwaGh0Z0).Additional information is available at www.asco.org/supportive-care-guidelines.
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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 | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".