F-030DEVELOPMENT OF A CLINICAL SCORE TO DISTINGUISH MALIGNANT FROM BENIGN OESOPHAGEAL DISEASE IN AN UNDIAGNOSED PATIENT POPULATION REFERRED TO AN OESOPHAGEAL DIAGNOSTIC ASSESSMENT PROGRAMME
Bibliographic record
Abstract
Objectives: Oesophageal cancer is associated with poor prognosis. Diagnosis is often delayed, resulting in presentation with advanced disease. We developed a clinical score to distinguish malignant from benign diagnoses in symptomatic patients prior to any diagnostic tests. Methods: Data from patients referred to a regional oesophageal diagnostic assessment programme between May 2013 and August 2016 prior to first clinical visit were analysed. Logistic regression was performed to identify predictors of malignancy based on patient characteristics and symptoms. Predicted probabilities were used to develop a score from one to 10 which was weighted according to beta coefficients for predictors in the model. Score accuracy was evaluated using a receiver operating characteristic curve and internally validated using bootstrapping techniques. Results: Of 530 patients, 363 (68%) were diagnosed with malignancy. Factors predictive of malignancy included: male (P < 0.001), family history of cancer (P=0.010) or oesophageal cancer (P=0.023), fatigue (P=0.004), chest/throat/back pain (P=0.001), age (P=0.040), melena (P=0.041), and weight loss (P=0.001). Dysphagia was the most common symptom (68%) but was not retained in the model (P=0.35). Malignancy predictors’ scores were: male, family history of oesophageal cancer, melena, 2 points each; family history of cancer, fatigue, chest/throat/back pain, and weight loss, 1 point each. For clinical application, patients were classified into low (1-2), medium (3-6), and high (7-10) risk. Low-risk patients had 70% lower chance of malignancy (RR = 0.28, 95% CI 0.21–0.38), medium-risk had 50% higher chance of malignancy (RR = 1.5, 95% CI 1.26–1.77), and high-risk patients were 8 times more likely to be diagnosed with malignancy (RR = 8.2, 95% CI 2.60–25.86). The AUC for malignancy was 0.82 (95% CI 0.77–0.87). Model fit for the bootstrapped and development models was good [x2(8, n = 530)=5.8, P=0.670]. Conclusions: A simple score using patient characteristics and symptoms reliably distinguished malignant from benign diagnoses. This score might be useful in expediting investigations and eventual diagnosis of malignancy. 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".