[P4–481]: A PROCEDURE TO CREATE A FRAILTY INDEX BASED ON ROUTINELY‐COLLECTED LABORATORY AND CLINICAL SAFETY DATA IN THE SETTING OF AN ALZHEIMER's DISEASE CLINICAL TRIAL
Bibliographic record
Abstract
Most individuals with dementia are over 75 years old and many are frail. Even so, most dementia clinical trials exclude frail adults, so whether their responses to treatment are similar to their non-frail counterparts is unclear. We previously showed that frailty can be quantified with a frailty index (FI) based on accumulation of deficits in health and can even be constructed from hospital blood work. Here our objective was to determine whether we can construct FIs based on laboratory and clinical safety data from the double blind Video Imaging Synthesis of Treating Alzheimer's disease (VISTA) galantamine trial. Subjects were 78.2 ± 7.8 years old (63.1% female). Three FIs were constructed following validated procedures. An FILAB was constructed from results of 35 standard laboratory tests (e.g. red cell, white cell & platelet counts, liver, kidney & thyroid function). Results within the normal range were scored as 0; values outside the normal range were scored as 1. Items were summed and divided by the number measured (e.g. 35) to yield an FI between 0–1. A similar approach was used to create an FICLINICAL based on 35 co-morbidities, physical factors plus neurological function data and an FICOMBINED was created from all 70 items. Results showed that the mean (± SD) FILAB scores for all patients at baseline were 0.093 ± 0.055 (n=129; range=0–0.306) whereas the average FICLINICAL scores were 0.213 ± 0.107 (n=130; range=0–0.457). The two FI scores were positively correlated (r=0.18; p= 0.045). When we combined both sets of deficits to create an FICOMBINED we found mean score of 0.153 ± 0.065 (n=130; range=0.028–0.314). These data demonstrate that: 1) an FI score can be constructed based on existing laboratory and/or clinical data that are routinely collected as safety data in the setting of an Alzheimer's drug trial; and 2) older individuals with a wide range of FI scores actually are recruited in drug trials, despite efforts to exclude them. Failure to consider varying frailty levels in drug trial participants is a missed opportunity to determine whether drugs work in patients who are most likely to take them.
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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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".