<i>Editorial Commentary:</i> A Patient‐Specific Approach to Preventing Venous Thromboembolism After Hip Arthroscopy Is Essential
Bibliographic record
Abstract
The incidence of hip arthroscopy (HA) has seen a dramatic rise over the past decade, with a bimodal distribution of patient age with peaks at both 18 and 42 years of age. Thus, it is essential to reduce complications, including venous thromboembolism (VTE), given reported incidences as high as 7%. Fortunately, more recent research, perhaps reflecting an evolution resulting in lower HA surgical traction times, has shown a VTE incidence of 0.6%. Perhaps because of such a low rate, recent research has also shown that generally, thromboprophylaxis does not significantly decrease the odds of VTE. The strongest predictors of VTE after HA are oral contraceptive use, prior malignancy, and obesity. Rehabilitation is also an important factor as some patients are ambulatory on postoperative day 1, reducing the VTE risk, whereas others require a few weeks of protected weight bearing, increasing their risk. A patient-specific approach to VTE prevention after HA, rather than a one-size-fits-all approach, is essential.
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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.023 | 0.023 |
| Insufficient payload (model declined to judge) | 0.016 | 0.017 |
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".