What Cardio-Oncology Lessons Can We Learn From Population-Based Data?
Bibliographic record
Abstract
T he maturation of administrative data- collected over the past few decades thanks to major advances in data collection processes and storage-has led to a rapid growth of analyses in which "big," "real-world" data are mined for epidemiologic associations.In this issue of JACC: CardioOncology, Bertero et al 1 present their findings from an analysis of administrative data from Puglia, Italy, in which adults at least 50 years of age with heart failure were matched to control subjects without heart failure to investigate the link between heart failure and the risk of developing cancer. 1 The study authors concluded that heart failure patients are at increased risk of incident cancer (including both solid organ and hematologic malignancies) and cancer mortality.To determine what can be robustly inferred from this finding and from analyses of big real-world data more broadly, one must carefully consider the limitations of administrative data. 2 The first limitation is unmeasured confounding.Key known cancer-causing exposures, such as obesity, heavy alcohol consumption, smoking, poor
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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 | 0.033 | 0.108 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.021 | 0.030 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
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".