PD09-04 TECHNOLOGY APPLICATIONS TO IMPROVE COMPLIANCE WITH HEMATURIA GUIDELINE RECOMMENDATIONS: ADDRESSING BARRIERS IN A LARGE ACADEMIC CENTER
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVE: Urological surgery is a known risk factor for venous thromboembolism (VTE).Low-dose low molecular weight heparin (LMWH) and direct oral anticoagulants (DOAC) may be reasonable alternatives for post-operative thromboprophylaxis in urology.We therefore performed a systematic review and network meta-analysis (NMA) of RCTs of these agents.METHODS: We searched Medline, Embase, and Central Cochrane library up to August 2018 to identify RCTs evaluating LMWH or DOAC, in head-to-head comparisons or compared with placebo or no treatment in adult patients undergoing noncardiac surgery.Primary outcome: symptomatic pulmonary embolism (PE).Secondary outcomes: symptomatic VTE, symptomatic proximal deep vein thrombosis (DVT), and major bleeding.Two authors independently identified studies from the search results and performed data extraction.We performed data synthesis by pairwise and network meta-analysis.We assessed the quality of studies using the Cochrane Collaboration risk of bias tool, and the quality of evidence with the GRADE approach for NMA.(PROSPERO CRD42018106181).RESULTS: We included 72 RCTs (62,792 patients) of which 56 involved orthopedic; 9 general, 4 thoracic, 2 gynecologic, and 1 urologic surgery.The majority of trials were of low risk of bias.Symptomatic PE was rare 0.24% whereas symptomatic VTE 0.66% and bleeding 0.86% were more common.Compared to low dose LMWH, DOACs may reduce symptomatic VTE (OR 0.49, 0.30-0.80),without increase in major bleeding (OR 1.13, 0.81-1.57).Both DOACs and LMWH increased major bleeding relative to placebo.See Table 1 for metaanalysis of primary and secondary outcomes and GRADE assessment of quality.CONCLUSIONS: Efficacy of LMWH or DOAC in reducing symptomatic PE could not be demonstrated.DOACs may be more effective than low-dose LMWH at reducing symptomatic VTE and proximal DVT.Limitations include low incidence of events and low number of non-orthopedic studies.This emphasizes the need of a RCT to provide efficacy data for symptomatic events in the urological setting.
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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 | 0.053 | 0.178 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".