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Management of suspected and confirmed recurrent venous thrombosis while on anticoagulant therapy. What next?

2019· review· en· W2955487344 on OpenAlexafffund
Marc Rodger, S. Miranda, Aurélien Delluc, Marc Carrier

Bibliographic record

VenueThrombosis Research · 2019
Typereview
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsOttawa Hospital
FundersHeart and Stroke Foundation of Canada
KeywordsAnticoagulant therapyMedicineVenous thrombosisThrombosisVenous thromboembolismAnticoagulantInternal medicineSurgery

Abstract

fetched live from OpenAlex

Suspected recurrent venous thromboembolism (VTE) is a common and vexing clinical problem. Confounding the diagnosis of recurrent VTE is a high frequency of residual VTE from prior VTE. The diagnosis of recurrent VTE must be established by comparing current imaging with past imaging to distinguish acute from chronic thrombosis. Next, we must ascertain if non-compliance was the cause of "apparent therapeutic failure" and if non-compliance is at play then re-initiate anticoagulant therapy. Therapeutic failure is relatively uncommon. As such, we must consider underlying causes of therapeutic failures including malignancy and potent thrombophilias. Finally, short term anticoagulant management of therapeutic failures is controversial, and requires further research, but the best current evidence supports a course of full-dose low-molecular-weight heparin (LMWH) (and dose escalated LMWH if failure occurs while on full-dose LMWH).

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.389
GPT teacher head0.479
Teacher spread0.090 · 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

Citations19
Published2019
Admission routes2
Has abstractyes

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