The utility of apparent diffusion coefficients for predicting treatment response to uterine arterial embolization for uterine leiomyomas: a systematic review and meta-analysis
Bibliographic record
Abstract
PURPOSE: Apparent diffusion coefficient (ADC) values, which are derived from diffusion-weighted imaging, have a potential role for predicting treatment response. A systematic review was conducted to examine the value of baseline ADC values for predicting leiomyoma size reduction after uterine arterial embolization (UAE). METHODS: Study selection, quality appraisal and data extraction were conducted independently by two authors. Statistical analyses included the calculation of weighted means and summary correlation coefficients (under the random effects model). RESULTS: Eleven studies consisting of a total of 258 patients (age, weighted mean±standard deviation [SD], 43.1±10.1 years) were included. The weighted mean±SD ADC value was 1.2±1.5 ×10-3 s/mm2 at baseline (ten studies) and 1.3±2.8 ×10-3 s/mm2 at approximately 6 months after embolization (six studies). The weighted mean percentage leiomyoma volume reduction (VR) at 6 months was 47.1%±35.6% (seven studies). Based on four studies, the weighted summary correlation coefficient for the correlation between baseline ADC and leiomyoma VR at approximately 6 months was not significant (r=0.40; 95% CI, -0.07 to 0.72; I2=69.7%). No associations were found in three of the four studies that examined changes in ADC values as a predictor. CONCLUSION: Due to high heterogeneity, it is unclear whether ADC may be useful for predicting treatment responses to UAE.
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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 | 0.023 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.033 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".