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How much does international normalized ratio monitoring cost during oral anticoagulation with a vitamin K antagonist? A systematic review

2009· review· en· W2049391968 on OpenAlexaff
S. Chambers, S. Chadda, JM Plumb

Bibliographic record

VenueInternational Journal of Laboratory Hematology · 2009
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsNexen (Canada)
Fundersnot available
KeywordsMedicineVitamin K antagonistPurchasing power parityHealth careTotal costOperations managementBusinessWarfarinAccountingFinanceEngineering

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.251
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.049
GPT teacher head0.385
Teacher spread0.336 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations25
Published2009
Admission routes1
Has abstractyes

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