A GENERIC METHODOLOGY FOR EFFECTIVE CREATION OF LABORATORY-TEST-BASED FRAILTY INDICES
Bibliographic record
Abstract
Abstract Laboratory test-based frailty indices (FI) are known to be highly predictive of adverse health outcomes and mortality. However, these FI depend on the proper classification of continuous valued health measurements into binary deficits (healthy or unhealthy). This classification is not standardized and is often done by using thresholds for medical intervention or maximally predictive values from statistical tests. This work proposes a simple and generic methodology for the creation of FI from laboratory values and measures its performance against existing methods. The methodology is as follows: a direction of risk is determined for each measurement, individuals are then assigned a score based on their relative standing in the population, binarization is then done using a global cut-point which binarizes all measures based on a given quantile. This method is shown to outperform FI created with medical risk thresholds for a range of global cut-points in both the NHANES and CSHA studies. Furthermore, our method is shown to be more robust to cohort effects than FI created using cut-points determined by maximum information measures, such as maximal separation of survival curves.
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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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".