Geriatric assessment-informed treatment decision making and downstream outcomes: what are the research priorities?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Geriatric assessment (GA) can predict outcomes relevant to patients and clinicians but is not widely used. The objective of this review is to summarize the evidence supporting use of GA to facilitate decision making and improve outcomes and identify gaps that need to be addressed to further bolster the rationale for the use of GA. RECENT FINDINGS: Recently several randomized controlled studies exploring the impact of GA-directed care have been reported. Although GA-directed care has not been shown to improve survival, it can decrease moderate to severe toxicity from chemotherapy, increase the likelihood of completing planned chemotherapy and improve quality of life without adversely affecting survival. In the surgical setting, GA-directed care may decrease duration of hospitalization, but does not affect rates of re-hospitalization. SUMMARY: GA-directed care can improve patient-important outcomes compared to usual care. However, more research on whether these findings apply to other contexts and whether GA-directed care can improve other outcomes important to patients, such as function and cognition, is needed. Also more clarity about how oncologic treatments should be modified based on results of a GA are needed if oncologists are to utilize this information effectively to obtain the reported results.
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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 | 0.023 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".