PS01.182: TOOLS FOR INDIVIDUALIZED SURVIVAL PREDICTION IN ESOPHAGEAL AND GASTROESOPHAGEAL JUNCTION CANCER
Bibliographic record
Abstract
Abstract Background Clinical, pathological and molecular information combined with cancer stage in prognostication algorithms can offer more personalized estimates of survival, which may guide treatment choices. Our aim was to evaluate the quality of prognostication tools in esophageal cancer. Methods We systematically searched MEDLINE & Embase from 2005- 2017 for studies reporting development or validation of models predicting long-term survival in esophageal cancer. We evaluated tools using the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies guidelines and the American Joint Committee on Cancer acceptance criteria for risk models. Results We identified 16 prognostication tools for patients treated with curative intent and one for patients with metastatic disease. These tools frequently excluded adenocarcinoma, contained outdated data and were developed with a limited sample size. Nine tools were developed in China for squamous cell cancer, and 11 used data on patients diagnosed prior to 2010. The majority of tools excluded key prognostic factors such as age and sex. Tumor stage and grade were the most commonly, but not universally, included factors. Twelve tools were designed to predict overall survival; five predicted cancer-specific survival. Bootstrap internal validation was performed for most tools; c-statistics ranged from 0.63–0.77 and graphically evaluated calibration was ‘good’. Five tools were externally validated; c-statistics ranged from 0.70–0.77. Conclusion Existing tools cannot be confidently used for esophageal cancer prognostication in current clinical practice. Better quality tools may help to more individually and accurately estimate disease course, select further treatments, and risk-stratify for future clinical trials. Disclosure All authors have declared no conflicts of interest.
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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 | 0.040 | 0.172 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".