Public articles linked to the same research event.
medRxiv Using harmonised clinical data from two Phase 3 trials (2,918 participants), this study trained monthly tabular models from baseline to therapy end for time-resolved prediction of end-of-therapy (EOT) outcomes and post-treatment relapse, finding that EOT outcome prediction improved after month 3 (ROC-AUC up to 0.84, driven by sputum-smear and solid culture), whereas relapse prediction among those with favourable EOT outcomes and completed follow-up improved only modestly through month 3 (ROC-AUC 0.58-0.63) before declining, with age, sex, clinical symptoms and bacterial burden contributing most; large language model-derived embedding models matched tabular relapse models throughout therapy and outperformed tabular EOT models at months 3 and 4 (ΔROC-AUC 0.12 and 0.
Using harmonised clinical data from two Phase 3 trials (2,918 participants), this study trained monthly tabular models from baseline to therapy end for time-resolved prediction of end-of-therapy (EOT) outcomes and post-treatment relapse, finding that EOT outcome prediction improved after month 3 (ROC-AUC up to 0.84, driven by sputum-smear and solid culture), whereas relapse prediction among those with favourable EOT outcomes and completed follow-up improved only modestly through month 3 (ROC-AUC 0.58-0.63) before declining, with age, sex, clinical symptoms and bacterial burden contributing most; large language model-derived embedding models matched tabular relapse models throughout therapy and outperformed tabular EOT models at months 3 and 4 (ΔROC-AUC 0.12 and 0.
Using harmonised clinical data from two Phase 3 trials (2,918 participants), this study trained monthly tabular models from baseline to therapy end for time-resolved prediction of end-of-therapy (EOT) outcomes and post-treatment relapse, finding that EOT outcome prediction improved after month 3 (ROC-AUC up to 0.84, driven by sputum-smear and solid culture), whereas relapse prediction among those with favourable EOT outcomes and completed follow-up improved only modestly through month 3 (ROC-AUC 0.58-0.63) before declining, with age, sex, clinical symptoms and bacterial burden contributing most; large language model-derived embedding models matched tabular relapse models throughout therapy and outperformed tabular EOT models at months 3 and 4 (ΔROC-AUC 0.12 and 0.
Using harmonised clinical data from two Phase 3 trials (2,918 participants), this study trained monthly tabular models from baseline to therapy end for time-resolved prediction of end-of-therapy (EOT) outcomes and post-treatment relapse, finding that EOT outcome prediction improved after month 3 (ROC-AUC up to 0.84, driven by sputum-smear and solid culture), whereas relapse prediction among those with favourable EOT outcomes and completed follow-up improved only modestly through month 3 (ROC-AUC 0.58-0.63) before declining, with age, sex, clinical symptoms and bacterial burden contributing most; large language model-derived embedding models matched tabular relapse models throughout therapy and outperformed tabular EOT models at months 3 and 4 (ΔROC-AUC 0.12 and 0.