Tinzaparin reduces health care resource use for anticoagulation in hemodialysis
Bibliographic record
Abstract
Anticoagulation is required during hemodialysis to prevent thrombus formation within the extracorporeal circuit. The low-molecular-weight heparin tinzaparin is more expensive than unfractionated heparin (UFH) in Canada but more convenient to administer. We conducted a time-and-motion study to test the hypothesis that tinzaparin may reduce nursing time and total health care costs compared with UFH. Data on health care resource use associated with anticoagulation during hemodialysis for chronic renal failure were collected at an academic hospital in Quebec. Nursing time was recorded for 8 nurses performing 16 dialysis sessions for 4 patients receiving tinzaparin and 4 receiving UFH (2 dialysis sessions per patient). Nurses had ≥ 1 year of experience supervising hemodialysis. We estimated total annual costs of nursing time and health care resources (anticoagulants, medical supplies, and laboratory testing) associated with anticoagulation. In sensitivity analyses, drug costs were varied ± 30% of their base-case values. Estimated annual nursing times per patient were 0.8 vs. 11.5 hours in the first year and 0.6 vs. 10.2 hours in subsequent years for tinzaparin vs. UFH, respectively. Annual drug costs per patient were CAD 898.56 for tinzaparin and 546.75 for UFH. Estimated total annual costs were CAD 1061.03 vs. 1012.71 in the first year and CAD 917.75 vs. 895.23 in subsequent years for tinzaparin vs. UFH, respectively. Use of tinzaparin was cost saving relative to UFH if tinzaparin price was reduced 30%. Most of the price differential between tinzaparin and UFH is offset by substantial time savings to nephrology nurses.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".