Additional file 1 of Ultrasound quadriceps muscle thickness is variably associated with frailty in haemodialysis recipients
Bibliographic record
Abstract
Additional file 1: Supplementary file 1. Description of how frailty, vulnerability and robustness defined. Supplementary Table 1. Frailty Phenotype. Supplementary Table 2. Frailty Index. Supplementary Table 3. Edmonton Frailty Scale. Supplementary Table 4a. Multiple linear regression of FP by BATT. Model 3. Supplementary Table 4b. Multiple linear regression of Frailty Phenotype by Low Muscle Mass. Model 3. Supplementary Table 4c. Multiple linear regression of Frailty Phenotype by Sarcopenia. Model 3. Supplementary Table 5a. Multiple linear regression of Frailty Index by BATT. Model 3. Supplementary Table 5b. Multiple linear regression of Frailty Index by Low Muscle Mass. Model 3. Supplementary Table 5c. Multiple linear regression of Frailty Index by Sarcopenia. Model 3. Supplementary Table 6a. Multiple linear regression of Edmonton Frailty Scale by BATT. Model 3. Supplementary Table 6b. Multiple linear regression of Edmonton Frailty Scale by Low Muscle Mass. Model 3. Supplementary Table 6c. Multiple linear regression of Edmonton Frailty Scale by Sarcopenia. Model 3. Supplementary Table 7a. Multiple linear regression of Clinical Frailty Scale by BATT. Model 3. Supplementary Table 7b. Multiple linear regression of Clinical Frailty Scale by Low Muscle Mass. Model 3. Supplementary Table 7c. Multiple linear regression of Clinical Frailty Scale by Sarcopenia. Model 3. Supplementary Table 8a. Multivariable logistic regression of Frailty Phenotype by BATT. Model 3. Supplementary Table 8b. Multivariable logistic regression of Frailty Phenotype by Low Muscle Mass. Model 3. Supplementary Table 8c. Multivariable logistic regression of Frailty Phenotype by Sarcopenia. Model 3. Supplementary Table 9a. Multivariable logistic regression of Frailty Index by BATT. Model 3. Supplementary Table 9b. Multivariable logistic regression of Frailty Index by Low Muscle Mass. Model 3. Supplementary Table 9c. Multivariable logistic regression of Frailty Index by Sarcopenia. Model 3. Supplementary Table 10a. Multivariable logistic regression of Edmonton Frailty Scale by BATT. Model 3. Supplementary Table 10b. Multivariable logistic regression of Edmonton Frailty Scale by Low Muscle Mass. Model 3. Supplementary Table 10c. Multivariable logistic regression of Edmonton Frailty Scale by Sarcopenia. Model 3. Supplementary Table 11a. Multivariable logistic regression of Clinical Frailty Scale by BATT. Model 3. Supplementary Table 11b. Multivariable logistic regression of Clinical Frailty Scale by Low Muscle Mass. Model 3. Supplementary Table 11c. Multivariable logistic regression of Clinical Frailty Scale by Sarcopenia. Model 3. Supplementary Table 12. Sensitivity Analyses of Frailty Score by multiple linear regression models in reverse order. Supplementary Table 13. Sensitivity analyses of multivariable logistic regression models of frailty. Multivariable models reversed.
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 | 0.002 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.820 | 0.085 |
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".