European perspectives in Thoracic Surgery, the ESTS venous thromboembolism (VTE) working group
Bibliographic record
Abstract
Venous thromboembolism (VTE), composed of deep vein thrombosis (DVT) and PE is a well-recognized cause for significant perioperative morbidity and mortality. While in orthopedic surgery and general oncology surgery there are well established guidelines regarding VTE prophylaxis, based on carefully conducted high level studies, in thoracic surgery the level of evidence and overall knowledge in the field is still lacking, The European Society of Thoracic Surgeons have established an international working group in 2016, whose task was the define contemporary best practice, coordinate research efforts and eventually define best practice guidelines. This collaboration has matured into a multi-organizational effort, with participation of the American Association for Thoracic Surgery, the International Society on Thrombosis and Haemostasis and Chinese and Japanese thoracic societies. Two major projects (International practice survey and an expert group Delphi process re best practice and VTE risk factors) have been completed so far. For 2018, the working group goals will be to establish VTE prophylaxis guidance in Thoracic Surgery.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".