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Record W1600604023 · doi:10.1186/cc4978

Measuring the anticoagulant effect of low molecular weight heparins in the critically ill.

2006· review· en· W1600604023 on OpenAlexafffund
Mark Crowther, Wendy Lim

Bibliographic record

VenueCritical Care · 2006
Typereview
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsSt. Joseph’s Healthcare HamiltonSt. Joseph's Hospital
FundersHeart and Stroke Foundation of CanadaSanofi
KeywordsMedicineAntithromboticCritically illLow molecular weight heparinIntensive care medicineIntensive care unitHeparinAnticoagulantInotropeAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

Antithrombotic prophylaxis in critically ill patients frequently fails. Venous thromboembolism is associated with adverse clinical outcomes, including a prolonged intensive care unit stay and death. A potential mechanism by which critically ill patients may be predisposed to antithrombotic failure is the inability to achieve 'prophylactic' anticoagulant drug levels as a result of impaired absorption. For example, previous studies have shown that patients on inotropes have reduced serum levels of low molecular weight heparin, presumably on the basis of reduced absorption from the subcutaneous injection site. In the previous issue of the journal, Rommers and colleagues examined whether subcutaneous edema reduces absorption of a low molecular weight heparin; although small, and thus underpowered, the authors failed to find any relationship between the level of low molecular weight heparin and the presence of edema. These findings provide reassurance that subcutaneously administered medications may be used in critically ill patients with edema.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.033
GPT teacher head0.348
Teacher spread0.315 · 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 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

Citations5
Published2006
Admission routes2
Has abstractyes

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