PRESERVING THE DISCRETENESS OF DEFICITS LEADS TO LOWER FRAILTY INDEX IN INDIVIDUALS LIVING IN LONG-TERM CARE
Bibliographic record
Abstract
Abstract The frailty index (FI), based on the deficit accumulation model, has potential to advance healthcare, but conventional coding of raw scores introduces noise. This study assesses the impact of the two different coding approaches on the FI. Two FI were calculated using 43 variables from 29,758 older (> 65 years old) Canadians adults (84.6 ± 8 years old; 64% female) living in long-term care. Scores were coded as 0, 0.5, or 1 regardless of the number of levels (grouped), or preserved (e.g., a 4 level variable was coded as 0, 0.33, 0.67, or 1; discrete). FI was correlated to age. Each ordinal variable was removed from the FI to further test the impact of the two coding approaches. The median FI for the grouped approach (0.302 (0.221 – 0.372)) was higher relative to the discrete approach (0.237 (0.170 - 0.307)). The discrete (r = .91) and grouped (r = .93) FI showed similar relationships to age. Removal of any ordinal variable reduced the grouped FI it by 0.004 or 0.016, whereas removal lead to both increases (range: 0.003 - 0.001) and reductions (range: 0.002 - 0.008) for the discrete FI. Using a grouped coding approach when quantifying frailty status artificially inflates FI among a large sample of older Canadians adults living in long-term care. The study underscores the importance of future FI research to adopt a discrete coding approach that accurately reflects the true level of impairment, for reducing noise in statistical models and better clinical utility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".