Incidental findings in patients screened for transcatheter aortic valve replacement: crystal ball or Pandora's box?
Bibliographic record
Abstract
According to the guidelines,1,2 transcatheter aortic valve replacement (TAVR) is recommended in patients who meet an indication for AVR and who have: (i) prohibitive risk (Class I) or high risk (Class IIa) for surgical AVR (SAVR) and (ii) a predicted post-TAVR survival >12 months. The guidelines also emphasize that when TAVR or high-risk SAVR is being considered, members of an interdisciplinary heart valve team should collaborate to provide optimal patient care. The discovery of incidental findings at the time of screening for TAVR poses some important challenges and dilemmas for the heart team, because it raises uncertainty about the patient's life expectancy following TAVR and thus about the futility of this intervention. The upside of incidental findings is that it may allow earlier detection of non-cardiovascular disease and therefore rapid implementation of potentially life-saving therapy. On the other hand, the downside is that these findings may lead to over-testing, over-diagnosis, and over-treatment of the non-cardiovascular disease and/or under-treatment of the cardiovascular disease, i.e. under-utilization of AVR in the case of patients with severe aortic stenosis.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".