Exercise prescription to reverse frailty
Bibliographic record
Abstract
Frailty is a clinical geriatric syndrome caused by physiological deficits across multiple systems. These deficits make it challenging to sustain homeostasis required for the demands of everyday life. Exercise is likely the best therapy to reverse frailty status. Literature to date suggests that pre-frail older adults, those with 1-2 deficits on the Cardiovascular Health Study-Frailty Phenotype (CHS-frailty phenotype), should exercise 2-3 times a week, for 45-60 min. Aerobic, resistance, flexibility, and balance training components should be incorporated but resistance and balance activities should be emphasized. On the other hand, frail (CHS-frailty phenotype ≥ 3 physical deficits) older adults should exercise 3 times per week, for 30-45 min for each session with an emphasis on aerobic training. During aerobic, balance, and flexibility training, both frail and pre-frail older adults should work at an intensity equivalent to a rating of perceived exertion of 3-4 ("somewhat hard") on the Borg CR10 scale. Resistance-training intensity should be based on a percentage of 1-repetition estimated maximum (1RM). Program onset should occur at 55% of 1RM (endurance) and progress to higher intensities of 80% of 1RM (strength) to maximize functional gains. Exercise is the medicine to reverse or mitigate frailty, preserve quality of life, and restore independent functioning in older adults at risk of frailty.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".