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Inferior vena cava filter use and patient safety: legacy or science?

2017· review· en· W2771256344 on OpenAlexaff
William Geerts, Rita Selby

Bibliographic record

VenueHematology · 2017
Typereview
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineInferior vena cava filterInferior vena cavaVena cavaVena cava filtersSurgeryRadiologyIntensive care medicineThrombosisVenous thrombosis

Abstract

fetched live from OpenAlex

There has been a dramatic increase in vena cava filter (VCF) use over the past 20 years in the absence of evidence that filters provide a net patient benefit or are required in most cases. This increase is largely attributable to the availability of retrievable filters and expanded indications, particularly as primary prophylaxis in patients thought to be at high risk of pulmonary embolism. Substantial variability in VCF use, unrelated to patient clinical factors, has been shown between hospitals, from region to region, and among various countries. Despite the lack of direct evidence for the benefit of VCFs for any indication, it is appropriate to insert a retrievable VCF in patients with a recent proximal deep vein thrombosis and an absolute contraindication to therapeutic anticoagulation and then to remove the filter once the bleeding risk decreases and the patient has been anticoagulated. Unfortunately, a high proportion of retrievable filters are not removed, even after the reason for their placement has long passed. Retrievable filters are associated with substantial rates of complications if they are not removed, including penetration of the vena caval wall, fracture and embolization of filter fragments, and caval occlusion. Patient safety priorities and medical-legal concerns mandate careful selection of patients for VCF placement and removal shortly after anticoagulation has been initiated.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.138
GPT teacher head0.394
Teacher spread0.256 · 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.

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

Citations14
Published2017
Admission routes1
Has abstractyes

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