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Record W4401326694 · doi:10.1016/j.rpth.2024.102540

Physician practice patterns on the use of inferior vena cava filters in venous thromboembolism

2024· article· en· W4401326694 on OpenAlexaff
Rahman Ladak, Massimo Sementilli, Jillian Calandra, Alejandro Lazo‐Langner, Deborah Siegal, Tzu‐Fei Wang, Rong Luo, Andrea Cervi

Bibliographic record

VenueResearch and Practice in Thrombosis and Haemostasis · 2024
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsWindsor Regional HospitalOttawa HospitalUniversity of WindsorUniversity of OttawaWindsor Clinical ResearchWestern University
Fundersnot available
KeywordsVenous thromboembolismInferior vena cavaMedicineVena cava filtersVena cavaVenous thrombosisIntensive care medicineCardiologyRadiologyInternal medicineThrombosis

Abstract

fetched live from OpenAlex

While anticoagulation remains the standard for treatment of acute deep vein thrombosis (DVT) and pulmonary embolism (PE), use of inferior vena cava filters (IVCFs) has increased in recent decades [1,2]. IVCFs were designed to trap thrombi originating in lower-extremity veins to prevent the development of clinically significant PE [2]. However, data demonstrating that IVCFs reduce thrombosis-related morbidity or mortality are lacking [2,3].

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)0.019
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.015
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.280
GPT teacher head0.450
Teacher spread0.170 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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