F-026 * VALIDATION OF A NEW APPROACH FOR MORTALITY RISK ASSESMENT IN OESOPHAGECTOMY FOR CANCER BASED ON AGE- AND GENDER-CORRECTED BODY MASS INDEX
Bibliographic record
Abstract
Objectives: We developed a new algorithm to identify high-risk patients for underweight after oesophagectomy for cancer. Patients are assigned to an age-gender-specific body mass index-percentile (AG-BMI) which is then used in a survival analysis. This model is able to identify more patients at risk for underweight in comparison to the classically used BMI. It shows a worse overall survival (OS) in patients with a preoperative AG-BMI <10th-percentile. The aim of this study is to validate this new model based on a cohort of patients from an external high-volume institution specialized in oesophageal cancer surgery. Methods: The validation cohort consists of 407 patients operated on between 1999 and 2012 with the prerequisite data to calculate AG-BMI and OS. The base cohort consisted of 642 consecutive patients, operated on in our institution between 2005 and 2010. Age, gender, height and weight on the day before surgery were used to calculate BMI and AG-BMI. OS was analysed and a multivariate analysis was performed. Results: Incidence rates of the AG-BMI <10th-percentile risk-patients in the validation cohort showed similar results to our original results (17.2% for both institutions) with a similar significant OS difference between at-risk-patients and not-at-risk-patients (P < 0.001). Multivariate analysis found the same five independent prognosticators for OS in both datasets: age, early versus advanced disease, resection status, number of positive lymph nodes and the AG-BMI-10th percentile, but not BMI itself. In the validation cohort, gender was identified as an additional independent prognosticator. The worse OS survival in AG-BMI <10th-percentile in both patient populations was related to a significantly higher number of deaths without oesophageal cancer recurrence. Conclusions: This study validates the newly developed AG-BMI model to predict more accurately a subgroup of patients at risk for worse survival after oesophagectomy. Improved perioperative identification of risk factors for poorer OS could help to develop perioperative strategies to reduce these risks. Disclosure: No significant relationships.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".