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Record W2031454492 · doi:10.1182/blood-2014-09-599498

Fondaparinux for the treatment of suspected heparin-induced thrombocytopenia: a propensity score–matched study

2014· article· en· W2031454492 on OpenAlexaff
Matthew Kang, Majed Alahmadi, Sonja Sawh, Michael J. Kovacs, Alejandro Lazo‐Langner

Bibliographic record

VenueBlood · 2014
Typearticle
Languageen
FieldMedicine
TopicHeparin-Induced Thrombocytopenia and Thrombosis
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsFondaparinuxHeparin-induced thrombocytopeniaMedicinePropensity score matchingHeparinInternal medicineIntensive care medicineThrombosisVenous thromboembolism

Abstract

fetched live from OpenAlex

Current guidelines for heparin-induced thrombocytopenia (HIT) management recommend heparin cessation and switching to a nonheparin anticoagulant (ie, argatroban, danaparoid) upon clinical suspicion. Fondaparinux may be effective but information supporting its use is limited. We retrospectively evaluated 239 patients who received a nonheparin anticoagulant (fondaparinux = 133, danaparoid = 59, and argatroban = 47) for suspected or confirmed HIT. A propensity score was constructed based on age, gender, creatinine, 4T scores, and comorbidity index, and used to match 133 patients to 60 controls. Outcomes were thrombosis or thrombosis-related death and major bleeding. In the matched population there were 22 (16.5%) episodes of thromboses in the fondaparinux group and 13 (21.4%) in the control group (χ(2) P = .424). Bleeding was observed in 28 (21.1%) patients in the fondaparinux group compared with 12 (20%) in the control group (χ(2) P = .867). Survival analysis, and subgroup and unmatched analyses showed similar results. In the fondaparinux group, 60% of patients received prophylactic doses. Fondaparinux has similar effectiveness and safety as argatroban and danaparoid in patients with suspected HIT. Prophylactic fondaparinux doses seem to be effective if no indication for full anticoagulation exists.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.092
GPT teacher head0.308
Teacher spread0.216 · 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 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

Citations136
Published2014
Admission routes1
Has abstractyes

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