What’s new in VTE risk and prevention in orthopedic surgery
Bibliographic record
Abstract
A State of the Art lecture titled "What's New in VTE Risk and Prevention in Orthopedic Surgery" was presented at the ISTH congress in 2019. Patients undergoing orthopedic surgery have long been recognized to be at increased risk of venous thromboembolism (VTE) and were among the first patient groups to be studied in VTE prophylaxis trials. From the late 1950s to 2010s, prophylaxis trials in major orthopedic surgery tended to focus on venographic deep vein thrombosis and assessed thromboprophylaxis in all patients based on a population approach. In general, anticoagulants were favored over mechanical prophylaxis or aspirin, and longer-duration prophylaxis was favored over shorter durations. As discussed in this paper, more recently, orthopedic prophylaxis has started to become more nuanced and individualized. Modern trials are focusing on symptomatic VTE as outcomes; there has been a resurgence in interest in aspirin for prophylaxis, and there has been a slow move to studying ways to evaluate VTE risk in patients undergoing orthopedic surgery and recommending thromboprophylaxis to patients based on individual attributes, in whom risk stratification and weighing of benefit versus risk of thromboprophylaxis is becoming key. We also touch on VTE risk and guideline recommendations to prevent VTE in 2 other commonly encountered orthopedic populations: patients undergoing knee arthroscopy and those with distal leg fractures. Finally, we summarize relevant new data on this topic presented during the 2019 ISTH annual congress in Melbourne.
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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".