Validation of the SF-6D Health State Utilities Measure in Lower Extremity Sarcoma
Bibliographic record
Abstract
Aim. Health state utilities measures are preference-weighted patient-reported outcome (PRO) instruments that facilitate comparative effectiveness research. One such measure, the SF-6D, is generated from the Short Form 36 (SF-36). This report describes a psychometric evaluation of the SF-6D in a cross-sectional population of lower extremity sarcoma patients. Methods. Patients with lower extremity sarcoma from a prospective database who had completed the SF-36 and Toronto Extremity Salvage Score (TESS) were eligible for inclusion. Computed SF-6D health states were given preference weights based on a prior valuation. The primary outcome was correlation between the SF-6D and TESS. Results. In 63 pairs of surveys in a lower extremity sarcoma population, the mean preference-weighted SF-6D score was 0.59 (95% CI 0.4-0.81). The distribution of SF-6D scores approximated a normal curve (skewness = 0.11). There was a positive correlation between the SF-6D and TESS (r = 0.75, P < 0.01). Respondents who reported walking aid use had lower SF-6D scores (0.53 versus 0.61, P = 0.03). Five respondents underwent amputation, with lower SF-6D scores that approached significance (0.48 versus 0.6, P = 0.06). Conclusions. The SF-6D health state utilities measure demonstrated convergent validity without evidence of ceiling or floor effects. The SF-6D is a health state utilities measure suitable for further research in sarcoma patients.
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 | 0.036 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".