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Record W2125475203 · doi:10.1177/1074248415601894

Extended-Duration Thromboprophylaxis Among Acute Medically Ill Patients

2015· review· en· W2125475203 on OpenAlexaff
Serge Korjian, Yazan Daaboul, Rim Halaby, Samuel Z. Goldhaber, Alexander T Cohen, Kiran Deep Singh, Ammu Susheela, Robert A. Harrington, Russell D. Hull, Adrian F. Hernandez, C. Michael Gibson

Bibliographic record

VenueJournal of Cardiovascular Pharmacology and Therapeutics · 2015
Typereview
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineObservational studyIntensive care medicineRandomized controlled trialVenous thromboembolismMajor bleedingEmergency medicineThrombosisInternal medicineAtrial fibrillation

Abstract

fetched live from OpenAlex

Acute medical illnesses are associated with a prolonged elevation in inflammatory markers that predisposes patients to thrombosis beyond the duration of their hospital stay. In parallel, both observational and randomized data have demonstrated a rate of postdischarge venous thromboembolic events that often exceeds that observed in the hospital setting. Despite this significant residual risk of venous thromboembolic events following discharge among acute medically ill patients, no therapeutic strategies have been recommended to address this unmet need. Available randomized trials have demonstrated the efficacy of extending the duration of thromboprophylaxis with available anticoagulants; however, the efficacy is offset, at least in part, by an increase in bleeding events. Identification of the optimal therapeutic strategies, treatment duration, and risk assessment tools that reconcile both efficacy and safety of extended-duration thromboprophylaxis among acute medically ill patients is an area of ongoing investigation.

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)0.001
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.345
Teacher spread0.312 · 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

Citations9
Published2015
Admission routes1
Has abstractyes

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