Sonidegib reduced tumor burden in patients with advanced basal cell carcinoma in the BOLT trial: Long-term analysis results
Bibliographic record
Abstract
Background The Hedgehog inhibitor sonidegib had durable efficacy and a manageable safety profile in patients with locally advanced basal cell carcinoma (laBCC) through 42 months of the BOLT trial. In this analysis, we characterize the effects of 200-mg and 800-mg sonidegib on tumors in patients in the BOLT trial. Methods Tumors were assessed using color photography and magnetic resonance imaging (MRI) by central and investigator review at baseline; Weeks 5, 9, and 17 after the start of treatment; then every 8 weeks during the first year; and every 12 weeks thereafter. Results In patients with laBCC receiving sonidegib, the decrease in best percentage change from baseline in target lesions was 92.3 % per central review and 96.7 % per investigator review in the 200-mg treatment arm, and 90.1 % per central review and 95.2 % per investigator in the 800-mg treatment arm. The kinetics of response to treatment appeared to influence tumor reduction, with patients responding within the first 3 months of treatment experiencing a greater decrease in tumor size over time than later responders. Additionally, patients whose best overall response to treatment was complete response or partial response had a generally longer duration of response compared with patients who had stable disease as best overall response. Tumor reduction and duration of response were greater when assessed by investigator review compared with central review. Conclusion Treatment with sonidegib for up to 42 months substantially reduced tumors in patients with laBCC.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".