How much does international normalized ratio monitoring cost during oral anticoagulation with a vitamin K antagonist? A systematic review
Bibliographic record
Abstract
Next-generation oral anticoagulants offer the potential for effective prevention and treatment of thrombosis without the need for repeated monitoring of the international normalized ratio (INR). This systematic review evaluated the costs associated with INR monitoring tests performed as part of the standard management of oral anticoagulation with vitamin K antagonists. Studies published in or after 1990 reporting the costs of INR monitoring were identified from bibliographic databases and manual searches of reference lists. Cost data were extracted and inflated to the year 2006 before purchasing power parity conversion to US dollars. A total of 29 studies reported the cost of one INR test, which was shown to range from $6.19 to $145.70. Cost estimates were based on various combinations of direct medical costs, such as healthcare contacts, equipment, laboratory tests, clerical costs (postage and stationery), telephone calls, quality control, training/education and patient transportation, and indirect costs, such as time lost from work. In conclusion, the cost of INR monitoring varied substantially between studies depending on the monitoring modality and setting, and the cost categories included. When selecting a published estimate, healthcare decision makers should ensure that the chosen estimate reflects local service provision as closely as possible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".