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Record W2913715509 · doi:10.1055/s-0039-1678720

Thromboprophylaxis in Patients with Acute Spinal Cord Injury: A Narrative Review

2019· review· en· W2913715509 on OpenAlexaff
Sam Schulman, Siavash Piran

Bibliographic record

VenueSeminars in Thrombosis and Hemostasis · 2019
Typereview
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsMcMaster UniversityThrombosis and Atherosclerosis Research Institute
Fundersnot available
KeywordsMedicinePulmonary embolismDeep veinThrombosisLow molecular weight heparinIncidence (geometry)Narrative reviewSpinal cord injuryVenous thromboembolismRegimenIntensive care medicineAnesthesiaSurgerySpinal cord

Abstract

fetched live from OpenAlex

Patients with acute spinal cord injury (SCI) have the highest risk of venous thromboembolism (VTE) among hospitalized patients. The incidence of total deep vein thrombosis ranges from 50 to 100% in untreated patients and pulmonary embolism is the third most common cause of mortality in these patients. The pathophysiology of the increased risk of VTE is explained by venous stasis after injury, endothelial vessel wall injury from surgery, and a hypercoagulable state associated with trauma. The current thromboprophylaxis options are limited, with low-molecular-weight heparin (LMWH) being the current standard of care. LMWH is commonly administered for 3 months, during which period the risk of VTE is especially high. Some uncertainty exists regarding the optimal timing to initiate pharmacological thromboprophylaxis and the best regimen of LMWH prophylaxis. High-quality data are currently lacking in thromboprophylaxis in patients with SCI. Many questions in this area remain to be answered, which are described in this narrative review.

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)0.003
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: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.048
GPT teacher head0.382
Teacher spread0.334 · 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
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

Citations22
Published2019
Admission routes1
Has abstractyes

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