A closer look at frailty in <scp>ESRD</scp>: Getting the measure right
Bibliographic record
Abstract
Patients treated with dialysis have low levels of physical functioning and activity. Whether this translates into frailty or not may depend on how the frailty phenotype is operationalized. This is a secondary analysis of data from the Renal Exercise Demonstration Project to evaluate two methods of operationalizing the Fried phenotype for frailty: Using measured walking speed and muscle weakness (FRAILmeas) and using substitution of the Physical Function Scale (PF) from the SF-36 questionnaire for walking speed and muscle weakness (FRAILsubst). Complete data for both measures were available for 188 hemodialysis patients. The frailty score (FRAILmeas) was the sum of criteria scores for measured gait speed, chair stand, body mass index, vitality, and physical activity. The frailty score (FRAILsubst) substituted the PF scale score (<75) as a surrogate measure for gait speed and for weakness. The frailty score ranged from 0 to 5. Scores ≥3 were categorized as frail, and <3 as not frail. The substitution of the PF score for walking speed and muscle weakness resulted in 78% of patients being categorized as frail compared to 24% using actual measured walking speed and muscle weakness (P < .001). The component of frailty that had the highest prevalence was low physical activity (average 54% of subjects). Frailty (using the FRAILmeas) was higher in patients with increasing age, female gender, and lower self-reported PF. Frailty is highly prevalent in hemodialysis patients; however, measured constructs of the components of frailty should be used to report the frailty phenotype.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".