Exploring the consistency of the SF-6Dv2 in a breast cancer population
Bibliographic record
Abstract
Background: Short-Form Six-Dimension version 2 (SF-6Dv2) is a multi-attribute utility instrument that can be used in combination with the SF-36v2 (SF-6Dv2SF-36) or as an independent instrument in two forms: six questions (SF-6Dv2ind-6) and 10 questions (SF-6Dv2ind-10). The purpose of this research was to assess the consistency between the results of the SF-6Dv2ind-6 and the SF-6Dv2SF-36 in patients with breast cancer.Research design and methods: This cross-sectional study was carried out on 418 patients with breast cancer. The degree of agreement between the descriptive systems of instruments was calculated using Spearman’s correlation coefficient, global consistency index (GCI), and identically classified index (ICI).Results: The average size of the Spearman’s correlation coefficients between the descriptive systems of instruments was higher than 0.5. The results of the GCI revealed that the level of agreement between dimensions of the two instruments had a mean of 64.9 (range 32.45–86.8). The SF-6Dv2SF-36 generates statistically higher values than does the SF-6Dv2ind-6, and mean difference between the two instruments was 0.087 for model 3 and 0.027 for model 10.Conclusions: This study provided evidence that the SF-6Dv2SF-36 and the SF-6Dv2ind-6 may produce different answers from patients with breast cancer and lead to a small difference in utility values.
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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".