New horizons in frailty: ageing and the deficit-scaling problem
Bibliographic record
Abstract
All the current frailty measures count deficits. They differ chiefly in which items, and how many, they consider. These differences are related: if a measure considers only a few items, to define broad risks those items need to integrate across several systems (e.g. mobility or function). If many items are included, the cumulative effect of small deficits can be considered. Even so, it is not clear just how small deficits can be. To better understand how the scale of deficit accumulation might impact frailty measurement, we consider how age-related, subcellular deficits might become macroscopically visible and so give rise to frailty. Cellular deficits occur when subcellular damage has neither been repaired nor cleared. With greater cellular deficit accumulation, detection becomes more likely. Deficit detection can be done by either subclinical (e.g. laboratory, imaging, electrodiagnostic) or clinical methods. Not all clinically evident deficits need cross a disease threshold. The extent to which cellular deficit accumulation compromises organ function can reflect not just what is happening in that organ system, but deficit accumulation in other organ systems too. In general, frailty arises in relation to the number of organ systems in which deficits accumulate. This understanding of how subcellular deficits might scale has implications for understanding frailty as a vulnerability state. Considering the cumulative effects of many small deficits appears to allow important aspects of the behaviour of systems close to failure to be observed. It also suggests the potential to detect frailty with less reliance on clinical observation than current methods employ.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".