Frailty Index to Measure Health Status in People with Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: To develop and validate, as a measure of overall health status, a Frailty Index (FI) for patients (n=1372) in the Canadian Scleroderma Research Group (CSRG) Registry. METHODS: Forty-four items were selected from the CSRG database as health deficits and recoded using FI criteria. To test construct validity, we compared measurement properties of the CSRG-FI to other FI, and related it to measures of damage, age, and time since diagnosis. To test criterion validity, we compared the baseline FI to that at last recorded visit and to mortality. RESULTS: The mean CSRG-FI was 0.33 with a sub-maximal limit of 0.67. In patients with diffuse disease, the mean was 0.38(SD 0.14); in patients with limited disease, the mean was 0.31(SD 0.13). The CSRG-FI was weakly (but significantly) correlated with the Rodnan Skin Score (r=0.28 in people with diffuse disease; 0.18 with limited) and moderately with the Physician Assessment of Damage (r=0.51 for both limited and diffuse). The risk of death increased with higher FI scores and with higher physician ratings of damage. The area under the receiver operating characteristic curve for the baseline FI in relation to death was 0.75, higher than for other measures (range: 0.57-0.67). CONCLUSION: The FI quantifies overall health status in people with scleroderma and predicts mortality. Whether the FI might help with decisions about who might best be served by more aggressive treatment, such as bone marrow transplantation, needs to be evaluated.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".