Has venous thromboembolism (VTE) prophylaxis made any difference for solid tumor inpatients at Princess Margaret Cancer Center (PM)?
Bibliographic record
Abstract
98 Background: VTE represents a significant risk for morbidity and mortality in cancer patients. Hospitalized solid tumor patients at PM are especially at risk (15% of inpatients experienced a VTE event in 2004 when prophylaxis rate was 19%). Institution-wide VTE prophylaxis policy was implemented, and prophylaxis uptake reached nearly 97% in Sept 2012. We are interested in whether the prophylaxis policy made any impact in reducing VTE incidences over time. Our objective is to compare inpatient VTE frequency before and after the implementation of the VTE prophylaxis policy (IPP) in Sept 2012 at PM. Methods: This retrospective study evaluated VTE frequency before and after IPP for all inpatients admitted to the 17A/B solid tumor units during the fiscal years 2007 to 2015. Eligible patients were those who became symptomatic and diagnosed 48 hours post hospital admission, as identified through the Discharge Abstract Database (DAD). Prophylaxis recipients were those without active bleeding and were not receiving treatment anticoagulant for a previous VTE. Results: 66 inpatients were identified to have developed VTE (table). The majority of inpatients who developed VTE had brain, lung, colorectal, pancreatic, or ovarian cancer. 83.3% of patients had a PADUA risk score of 4 or greater. From April 2007 to Sept 2012, 1 patient developed VTE every 105.7 patient visits (48 patients developed VTE in 5075 patient visits). From Sept 2012 to March 2015, 1 patient developed VTE every 112.6 patient visits (18 patients developed VTE in 2026 patient visits). Odds ratio of 0.9388 (CI = 0.5448-1.6177) showed lower VTE risk for patients on prophylaxis during admission. Conclusions: There appeared to be a decreasing trend in the frequency of VTE post-IPP, for cancer inpatients within a limited population. [Table: see text]
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".