The 'Sit-to-Scale' score a pilot study to develop an easily applied score to follow functional status in elderly dialysis patients
Bibliographic record
Abstract
In Canada, the number of older persons requiring treatment for end-stage renal disease (ESRD) rose by nearly 20% between 1997 and 2001 [ 1 ]. Fifty-five percent of all new dialysis patients are 65 years of age and older, and almost 25% are 75 years and older [ 2 ]. Similar demographic trends have been observed in the United States and the United Kingdom [ 3 , 4 ]. Cross-sectional studies have shown older haemodialysis patients are generally less active and more physically impaired than younger patients [ 3 , 5 , 6 ]. In addition older dialysis patients suffer from a high degree of disability and functional dependence [ 6–9 ]. Functional impairment is important, clinically, for many reasons. In the general population, functional impairment predicts falls, fractures and hospitalization [ 10–12 ]. In the dialysis population clinical outcomes such as falls or fractures are very common, with studies suggesting that over 45% of older dialysis patients experience one or more falls each year [ 12–15 ]. In fact, in a small prospective study, we have shown that functional measures such as gait speed may better predict those individuals at higher risk of fractures when compared radiological evaluation [ 16 ]. Furthermore, intervention strategies, such as exercise or rehabililation programs, are known to be effective in dialysis [ 17–20 ].
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".