An Easy-to-Implement Clinical-Trial Frailty Index Based on Accumulation of Deficits: Validation in Zoster Vaccine Clinical Trials
Bibliographic record
Abstract
Purpose: Despite being among those most in need of protection, frail older adults are often not well represented in clinical trials. Although frailty likely influences responses to treatments and vaccines, frailty may not be explicitly considered in trials even when frail participants are enrolled due to the perception that frailty is difficult to measure effectively and efficiently without adding to participant or data collection burden. We developed an easy-to-implement frailty index, the Clinical Trial-Frailty Index (CT-FI), based on baseline medical history and standard patient-reported outcomes using data from clinical trials of recombinant Zoster vaccine (the ZOE-50 and ZOE-70 studies). Our objective was to demonstrate that the CT-FI is a robust measure that may be used retrospectively or prospectively in clinical trials where sufficient patient data have been collected. Methods: The CT-FI was based on baseline medical history and Quality of Life questionnaires (SF-36 and EQ-5D). Items meeting criteria for inclusion were scored from 0 to 1, then summed for each participant and divided by the total number of deficits considered. Validation analyses included descriptive verification of distribution and age- and sex-associations in relation to usual patterns of the frailty index, regressions in relation to outcomes hypothesized to be related to frailty, and resampling methods within the index. Results: The CT-FI distribution was well represented by a gamma distribution with a range of 0-0.70. Deficit accumulation increased with chronological age and was higher for females. Multivariate Cox regression survival analysis showed that the CT-FI, age, and sex were significant predictors of mortality. Jackknife and Bootstrap resampling methods highlighted the robustness of the CT-FI, which was not sensitive to inclusion/exclusion of specific individual or groups of variables. Conclusion: We have developed a reliable, robust and easy-to-implement CT-FI with potential retrospective or prospective application in other clinical trials.
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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 | 0.244 | 0.339 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".