Predicting risk of mortality in dialysis patients: prognostic value of a simple chest x-ray
Bibliographic record
Abstract
Patients with kidney failure on dialysis are at high risk for cardiovascular disease and premature death in aggregate. Individual patient risk, however, varies widely. Improved ascertainment of individual risk could inform decisions about patient management and counselling. Since the majority of mortality of patients is driven by cardiovascular (CV) causes, CV risk factors such as heart size and aortic calcification are plausible prognostic markers. The objective of this study was to assess the value of simple, chest X-ray derived measures of cardiac size (Cardiothoracic Ratio) and vascular calcification (Aortic Arch Calcification), in predicting death in a prevalent cohort of hemodialysis (HD) patients. Employing the Manitoba Renal Database, all patients starting dialysis in Manitoba from 2000-2010 and who received a chest X-ray were identified. Cardiothoracic ratio and aortic calcification values were determined by two independent reviewers for 824 prevalent patients. The goals of the student were to 1) learn how to use a medical database 2) develop clinical chest X-ray reading skills and 3) become familiar with and use appropriate statistical tools to determine whether cardiothoracic ratio, aortic arch calcification, or both, were predictors of mortality, and whether they improved upon simpler prognostic models.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".