Retrospective analysis of preoperative chemoradiation therapy for esophageal and gastroesophageal junction (GEJ) cancer: Lessons to be learned from expanding inclusion criteria in this patient population.
Bibliographic record
Abstract
214 Background: Preoperative chemoradiation therapy utilizing paclitaxel, carboplatin and radiation as per the CROSS Group increased survival rates among patients with potentially curable esophageal or GEJ cancer. A retrospective chart review was performed to determine: 1) the frequency of patients meeting the original trial inclusion/exclusion criteria 2) overall survival (OS), disease free survival (DFS), and pathological response rates (RR) of this patient population. Methods: Data was collected on 89 patients who received preoperative chemoradiation therapy (CROSS protocol) in Edmonton, Alberta between June 1, 2010 and April 11, 2014. Mean and standard deviation were calculated for continuous data and frequency (proportion) for categorical data. Time to event was presented using Kaplan-Meier estimates and log rank tests were used to compare the KM curves. Results: The median age of the entire patient population was 62 years with 77 (86.5%) of the patients being male. Twenty-three (26%) patients met inclusion criteria (MIC), whereas 66 (74%) patients failed to meet inclusion criteria (FMIC). Reasons for FMIC included: clinical stage (39.3%); weight loss > 10% (39.0%); previous cancer history (22.5%); tumor length (9.0%); and age (5.6%). Pathological complete response (CR) occurred in 24% of all patients with a trend to increased CR in MIC vs FMIC (35% vs 20%; p=0.18). Significant improvement in DFS was observed in the MIC group compared to FMIC (p=0.004). Although a trend to improvement was seen for OS in the MIC group, it is not yet significant at this early analysis (p=0.12). Conclusions: A significant proportion of patients treated with neoadjuvant chemoradiation therapy for esophageal and GEJ cancer at our center did not meet the inclusion/exclusion criteria utilized in the CROSS trial. This had a detrimental impact on FMIC patients questioning its benefit and utility in a broader patient population. [Table: see text]
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".