Geriatric assessment domains as predictors for clinical endpoints in older adults with cancer: Protocol for an updated systematic review
Bibliographic record
Abstract
Geriatric assessments (GA) are increasingly used to inform treatment decision making and tailoring supportive care for older adults with cancer. Identifying which domains predict clinically relevant outcomes might be particularly useful for risk stratification in settings where a GA is not available and/or feasible. The objective of this updated systematic review is to evaluate individual GA domains as predictors for mortality and treatment-related outcomes. Eligible studies will be identified using a predefined search strategy developed in collaboration with an expert librarian in electronic databases (Medline, Cochrane, Embase, CINAHL) and comprise peer-reviewed papers published in any language from July 2017 and reporting on the prospective association between individual GA domains and mortality as well as surgical- or systemic treatment-related outcomes in older adults with cancer. All title/abstract screening, full-text screening, and data extraction will be performed independently by at least 2 authors. Information on cut-offs of GA domains will also be extracted to assess for variability across studies. A decision on performing a meta-analysis versus a narrative summary will be made based on predetermined criteria, which will include heterogeneity among studies and variability in GA tools and cutoff used for each individual domain, as well as level of risk of bias. If a meta-analysis is indicated, a random effects meta-analysis will be conducted using the Comprehensive Meta-Analysis software. The review will be reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. This protocol has been registered with PROSPERO (ID: CRD42024580404). This review seeks to investigate individual GA domains as predictors for patient- and treatment-related outcomes. Findings may inform efforts on optimizing GA for 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 | 0.061 | 0.083 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.021 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.056 | 0.008 |
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".