EACTS/ESCVS best practice guidelines for reporting treatment results in the thoracic aorta
Bibliographic record
Abstract
Summary Endovascular treatment of the thoracic aorta (TEVAR) is rapidly expanding, with new devices and techniques, combined with classical surgical approaches in hybrid procedures. The present guidelines provide a standard format for reporting results of treatment in the thoracic aorta, and to facilitate analysis of clinical results in various therapeutic approaches. These guidelines specify the essential information and definitions, which should be provided in each article about TEVAR: Definitions of disease conditionsExtent of the diseaseComorbiditiesExact demographics of the patient materialDescription of the procedure performedDevices which were utilizedMethods for reporting early and late mortality, and morbidityReinterventions and additional proceduresStatistical evaluation It is hoped that strict adherence to these criteria will make the future publications about TEVAR more comparable, and will enable the readership to draw their own, scientifically validated conclusions about the reports.
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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 | 0.047 | 0.164 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.024 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".