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Record W2116393924 · doi:10.1309/ajcp8o8gipppnush

Critical Values in the Coagulation Laboratory

2011· article· en· W2116393924 on OpenAlexafffund
Menaka Pai, Karen A. Moffat, Elizabeth Plumhoff, Catherine P.M. Hayward

Bibliographic record

VenueAmerican Journal of Clinical Pathology · 2011
Typearticle
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsMcMaster UniversityHamilton Regional Laboratory Medicine Program
FundersMcMaster University
KeywordsProthrombin timePartial thromboplastin timeCoagulation testingWorkloadMedicineCoagulationStatisticsFibrinogenInternal medicineMathematicsComputer science

Abstract

fetched live from OpenAlex

Critical values are vital to safe clinical and laboratory practice. To address the lack of information on critical values in coagulation, pattern-of-practice surveys were distributed to members of the North American Specialized Coagulation Laboratory Association. More than 70% of respondents had critical values for commonly performed tests. Median values were as follows: prothrombin time, more than 37 seconds; international normalized ratio, more than 5; activated partial thromboplastin time, more than 100 seconds; and fibrinogen level, less than 100 mg/dL. Critical value reporting generated a significant workload, with up to 15% of these tests yielding critical results. The median time to report critical values was 7 minutes for inpatients. Despite the lack of guidelines surrounding critical values in coagulation, this survey confirms that laboratories have reasonable and uniform practices. It also provides critical value medians and ranges for a wide range of tests. Laboratories without critical values or in the process of reviewing their values may find this survey of their peers useful.

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 imitation

Not 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.

metaresearch head score (Codex)0.041
metaresearch head score (Gemma)0.153
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.041
Threshold uncertainty score0.219

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0410.153
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0020.006
Scholarly communication0.0070.005
Open science0.0020.003
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0040.002

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 head0.170
GPT teacher head0.497
Teacher spread0.327 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations28
Published2011
Admission routes2
Has abstractyes

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