Decreasing rates of venous thromboembolism after radical cystectomy: an encouraging trend and more work is still needed
Bibliographic record
Abstract
Venous thromboembolism (VTE) following major cancer surgery is a significant cause of preventable morbidity and mortality.VTE is the leading cause of non-cancer related death after abdominopelvic surgery for cancer, and improvements in risk assessment as well as strategies for prevention and treatment are needed (1).In this study by Lyon et al. published in May 2018, the temporal trend in VTE rates after radical cystectomy were assessed using the American College of Surgeons' National Surgical Quality Improvement Program (NSQIP) (2).This study reported decreasing rates of VTE after radical cystectomy from 5.1% in 2011 to 2.8% in 2016 (2).The authors also identified independent risk factors for VTE after cystectomy including active malignancy, long operative time, obesity and infection (2).These findings are consistent with prior literature (3).Risk factors for VTE have previously been studied and have been incorporated into risk assessment tools that surgeons may use to risk stratify patients.Unfortunately, some of these tools may be challenging to use due to the multitude of variables they include, or may not sufficiently account for operative factors leading to insufficient risk stratification for some procedures (3).Preventing VTEs is important, and this study appears to demonstrate that urologists have been successful in reducing VTE rates after cystectomy: the urological procedure historically associated with the highest rate of VTE (3).Prevention of VTEs has been identified as a focus for quality improvement programs in post-operative Cite this article as: McAlpine K, Lavallée LT.Decreasing rates of venous thromboembolism after radical cystectomy: an encouraging trend and more work is still needed.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".