Meta-analysis of health state utility values measured by EuroQol 5-dimensions (EQ5D) questionnaire in Chinese women with breast cancer
Bibliographic record
Abstract
Abstract Background To synthesise EQ5D health state utility values in Chinese women with breast cancer for parameterising a cost utility model. Methods Eligible studies had to report health state utility values measured by EQ-5D in Chinese women diagnosed with breast cancer. Risk of bias was assessed using the Newcastle Ottawa Scale (NOS). Data from single arm studies was pooled using meta-analysis of single proportions to provide overall point estimates and 95% confidence intervals for fixed and random effects models using the inverse variance and Der Simonian-Laird methods respectively. Heterogeneity was evaluated using the I 2 statistic and sensitivity analysis and meta-regression were conducted. Results Five papers were included, when all studies were combined (n = 4,100) the mean utility (95% confidence interval) for random effects model was 0.83 (0.78, 0.89); for TNM 0-1 0.85 (0.75, 0.95); for TNM II 0.85 (0.78, 0.93); for TNM III 0.83 (0.77, 0.90) and for TNM IV 0.73 (0.63, 0.82).The utility of patients in State P (first year after primary breast cancer) 0.84 (0.80, 0.88); in State R (first year after recurrence) 0.73 (0.69, 0.76), in State S (second and following years after primary breast cancer or recurrence) 0.88 (0.83, 0.92); and in State M (metastatic disease) 0.78 (0.74, 0.82). Mean utility for duration since diagnosis 13 to 36 months was 0.88 (0.80, 0.96, I 2 =95%); for 37 to 60 months 0.89 (0.82, 0.96, I 2 =90%); for more than 60 months 0.86 (0.76, 0.96, I 2 =90%). Mean utility for chemotherapy was 0.86 (0.79, 0.92, I 2 =97%); for radiotherapy 0.83 (0.69, 0.96, I 2 =97%); surgery 0.80 (0.69, 0.91, I 2 =98%); concurrent chemo-radiation 0.70 (0.60, 0.81, I 2 =86%) and endocrine therapy 0.90 (0.83, 0.97, I 2 =91%). Conclusion: This study synthesises the evidence for health state utility values for Chinese women with breast cancer which is useful to inform cost utility models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".