In-hospital risk of venous thromboembolism and bleeding and associated costs for patients undergoing total hip or knee arthroplasty
Bibliographic record
Abstract
OBJECTIVE: Benefits of anti-coagulation for venous thromboembolism (VTE) prevention in total hip and knee arthroplasty (THA/TKA) may be offset by increased risk of bleeding. The aim was to assess in-hospital risk of VTE and bleeding after THA/TKA and quantify any increased costs. METHODS: Healthcare claims from the Premier Perspective(TM) Comparative Hospital Database (January 2000-September 2008) were selected for subjects ≥ 18 years with ≥ 1 diagnosis code for THA/TKA. VTE was defined as ≥ 1 code for deep vein thrombosis or pulmonary embolism. Bleeding was classified as major/non-major. Incremental in-hospital costs associated with VTE and bleeding were calculated as cost differences between inpatients with VTE or bleeding matched 1:1 with inpatients without VTE or bleeding. RESULTS: A total of 820,197 inpatient stays were identified: 8042 had a VTE event and 7401 a bleeding event (2740 major bleeding). The risks of VTE, any bleeding, and major bleeding were 0.98, 0.90, and 0.33/100 inpatient stays, respectively. Mean incremental in-hospital costs per inpatient were $2663 for VTE, $2028 for bleeding, and $3198 for major bleeding. LIMITATIONS: These included possible inaccuracies or omissions in procedures, diagnoses, or costs of claims data; no information on the amount of blood transfused or decreases in the hemoglobin level to evaluate bleeding event severity; and potential biases due to the observational design of the study. CONCLUSIONS: In-hospital risk and incremental all-cause costs with THA/TKA were higher for VTE than for bleeding. Despite higher costs, major bleeding occurred less frequently than VTE, suggesting a favorable benefit/risk profile for VTE prophylaxis in THA/TKA.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".