143 A Comparison of the Tumour Response in Esophageal Cancer Patients Treated with Tri-Modality Approach Using Either Cisplatin/5-FU or Carboplatin/Paclitaxel and Concomitant Radiation Therapy
Bibliographic record
Abstract
CARO-ASM 2019 of overall survival (OS), locoregional recurrence (LRR), locoregional recurrence-free survival (LRFS), and unexpected Grade 3 toxicity.Multivariable analysis was performed for OS and LRFS.Results: Overall, 123/300 (41%) patients experienced a RTC.Among those with RTC, 2-year results were: OS 74%, LRR 4.2%, LRFS 73%, and unexpected Grade ≥3 toxicity 5.7%.Among those without RTC, analogous figures were: 84%, 1.7%, 84%, and 3.9%, respectively.The differences were statistically significant for OS (p=0.033) and LRFS (p=0.015), but not for LRR (p=0.22) or toxicity (p=0.48).Among RTC components, only RT interruption was associated with worse OS (OR 1.82, CI 95% 1.04 -3.17, p=0.035) and LRFS (OR 1.95, CI 95% 1.13 -3.37, p=0.017) on univariable analysis.On multivariable analysis, variables (HR, 95% CI, p-value) associated with worse OS were: RTC (2.08, 1.11-3.91,0.023), ECOG performance status ≥1 (5.11, 2.55-10.26,<0.001), N2/3 disease (2.83, 1.38-5.8,0.0044) and treatment modality other than cRT: RT alone (3.2, 1.39 -7.38, 0.0064), surgery with postoperative cRT (20.41 , 6.99 -59.6, <0.001), and surgery with postoperative RT (4.28, 1.72 -10.68, 0.0018).Similar results were seen for LRFS. Conclusions:The association of a rocky treatment course with worse oncologic outcomes in HNC has been independently validated and could be used as an intermediate outcome variable in future prospective studies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".