Predicting impending death: Inconsistency in speed is a selective and early marker.
Bibliographic record
Abstract
Among older adults, deficits in both level and variability of speeded performance are linked to neurological impairment. This study examined whether and when speed (rate), speed (inconsistency), and traditional accuracy-based markers of cognitive performance foreshadow terminal decline and impending death. Victoria Longitudinal Study data spanning 12 years (5 waves) of measurement were assembled for 707 adults aged 59 to 95 years. Whereas 442 survivors completed all waves and relevant measures, 265 decedents participated on at least 1 occasion and subsequently died. Four main results were observed. First, Cox regressions evaluating the 3 cognitive predictors of mortality replicated previous results for cognitive accuracy predictors. Second, level (rate) of speeded performance predicted survival independent of demographic indicators, cardiovascular health, and cognitive performance level. Third, inconsistency in speed predicted survival independent of all influences combined. Fourth, follow-up random-effects models revealed increases in inconsistency in speed per year closer to death, with advancing age further moderating the accelerated growth. Hierarchical prediction patterns support the view that inconsistency in speed is an early behavioral marker of neurological dysfunction associated with impending death.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".