The Euro Heart Survey on atrial fibrillation: a picture and a thousand wordsThe opinions expressed in this article are not necessarily those of the Editors of the European Heart Journal or of the European Society of Cardiology.
Bibliographic record
Abstract
This editorial refers to ‘Atrial fibrillation management: a prospective survey in ESC member countries. The Euro Heart Survey on atrial fibrillation’† by R. Nieuwlaat et al., on page 2422 The Euro Heart Survey on atrial fibrillation (AF) is a registry of patients in Europe who were seen by a cardiologist for AF over a 12-month period in 2003 and 2004. In the data presented,1 we see a snapshot of several aspects of AF at this particular moment in time in Europe. Registries are an important tool for description of any number of aspects of a particular medical condition, including its management. Their strength lies in the fact that data are provided on a large number of patients and the cross-sectional aspects of the data are advantageous; in this case, the number of countries is also large. Limitations include the degree of adherence to definitions, rigour, completeness and uniformity of data collection, and potential selectivity in patient enrolment (consecutiveness) among others. Nevertheless, they are useful for description of practice patterns among a group of physicians, for development of proposals to modify physician behaviour, and for identifying hypotheses to be tested in randomized, controlled clinical trials. Although subsequent follow-up data from the survey are expected, for now the authors have focused on describing the practices of European cardiologists and contrasting their findings with some of the recommendations of the AF treatment guidelines.2 It is often said that ‘a picture is worth a thousand words’. In this case, the metaphor of the registry as a picture supports this adage in the number of words needed to describe and discuss the picture we are being shown.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".