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Record W2497772732 · doi:10.1093/ajcp/aqw121

An Assessment of the State of Current Practice in Coagulation Laboratories

2016· article· en· W2497772732 on OpenAlexaff
Nicole D. Zantek, Catherine P.M. Hayward, Trevor Simcox, Kristi J. Smock, Peihong Hsu, Elizabeth M. Van Cott

Bibliographic record

VenueAmerican Journal of Clinical Pathology · 2016
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCoagulation testingApixabanDabigatranRivaroxabanMedicinePartial thromboplastin timeStaffingCoagulationProthrombin timeOvercrowdingIntensive care medicineMedical emergencySurgeryWarfarinInternal medicineAtrial fibrillation

Abstract

fetched live from OpenAlex

OBJECTIVES: To assess the state of current practice in coagulation laboratories regarding three pressing issues: staffing, handling Ebola specimens, and testing/billing for tests that measure direct oral anticoagulants (DOAC). METHODS: A survey and analysis of specialized coagulation laboratories in North America was conducted. RESULTS: Approximately 4,000 special coagulation tests-per-technologist-per-year was rated as either a "good" staffing level or "adequate-but-ideally-need-more" employees. Requiring technologists to perform more than that was rated as an "inadequate" staffing level. For Ebola patients, coagulation testing is mostly performed by point-of-care. Only 26.1% would perform coagulation tests for Ebola specimens within their laboratory (rather than at the bed side or a separate designated space outside the laboratory). Coagulation tests offered for Ebola patients were limited: prothrombin time (63.0% of laboratories), activated partial thromboplastin time (37.0%), D-dimer (13.0%), and fibrinogen (8.7%); 26.1% of laboratories did not offer any coagulation tests for Ebola patients. Approximately 35% of special coagulation laboratories bill for at least one laboratory test for DOACs: 33% bill for an anti-Xa calibrated with rivaroxaban, 17% bill for an anti-Xa calibrated with apixaban, and 27% bill for at least one of several tests for dabigatran. Approximately 48% do not offer any tests for DOACs. CONCLUSIONS: These results may help laboratories negotiate for additional technologists if needed, prepare for Ebola specimens, and manage the demand for laboratory tests for new DOAC anticoagulants.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.431
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.068
GPT teacher head0.560
Teacher spread0.492 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations16
Published2016
Admission routes1
Has abstractyes

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