Bibliographic record
Abstract
Every man desires to live long, but no man would be old. Understanding frailty—the concept of vulnerability to adverse health outcomes of people of the same chronological age—continues to motivate research by geriatricians, epidemiologists, sociologists and laboratory-based scientists. While some clinicians have embraced frailty as the Holy Grail of geriatric medicine [1] or advocated it as another Geriatric Giant [2], others remain unconvinced about the feasibility of applying frailty measures in routine practice [3]. The potential to measure with precision the vulnerability of older people has been met with skepticism [4]. Some geriatricians suspect that objective frailty measures are meant to undermine clinical judgement, somehow reflecting an erosion of trust between patients and their doctors [5]. Moreover, efforts to underpin geriatric medicine with more scientific rigour have not been accompanied by reduced antipathy to our specialty [6]. The frailty index (FI), or deficit accumulation model, is one of the three main approaches to the measurement of frailty. It conceptualises frailty as a multidimensional risk state, which can be measured by the quantity rather than by the nature of health problems; individuals accumulate deficits throughout their lives and the more things individuals have wrong with them, the higher the likelihood they will be frail [7]. The FI employs a well-defined methodology (e.g. someone with 6 deficits out of 40 counted has a FI of 0.15). Alternative approaches are to identify frailty as a clinical syndrome or phenotype (such as that defined by Fried et al. [8] as the presence of ≥3 of 5 criteria: weight loss, exhaustion, weak grip strength, slow walking speed and low physical activity) or the measurement of frailty based on the clinician's subjective opinion [9]. The former predicts adverse outcomes in large population samples [10]. The latter have strong face validity, but rely on judgement and depend on geriatric expertise (e.g. accurate assessment of functional status) limiting their generalisability.
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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 | 0.046 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.030 | 0.035 |
| Insufficient payload (model declined to judge) | 0.010 | 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".