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Record W4237529142 · doi:10.3324/haematol.2019.234666

Recurrent stroke: the role of thrombophilia in a large international pediatric stroke population

2019· erratum· en· W4237529142 on OpenAlexaff
Gabrielle deVeber, Fenella J. Kirkham, Kelsey Shannon, Leonardo R. Brandão, Ronald Sträter, Gili Kenet, Hartmut Clausnizer, Mahendranath Moharir, Martina Kausch, Rand Askalan, Daune MacGregor, Monika Stoll, Antje Torge, Nomazulu Dlamini, Mara Prengler, Jaspal Singh, Ulrike Nowak‐Göttl

Bibliographic record

VenueHaematologica · 2019
Typeerratum
Languageen
FieldMedicine
TopicBlood Coagulation and Thrombosis Mechanisms
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsThrombophiliaStroke (engine)MedicinePopulationPediatric strokePhysical medicine and rehabilitationPediatricsPhysical therapyInternal medicineIschemic strokeThrombosisEnvironmental health

Abstract

fetched live from OpenAlex

In the article pre-published online on January 24, 2019 and published in the paper version of Haematologica [volume 104(8):1676-1681; doi:10.3324/haematol2018.211433] we have to correct: • that recurrent stroke occurred in 160/872 (instead of 160 / 880) children [page 1678, second column, line 6]. • the incidence rates of recurrent AIS with respect to the individual exposure time in years given in the abstract (page 1676, lines 16-18) and in the results section (page 1679, paragraph "prothrombotic risk factors", lines 38-40). As explained in the methods section, we calculated the absolute risk of AIS recurrence as incidence rates per 100 patient-years (%). According to the individual exposure times (years) to antithrombin, lipoprotein (a) and the presence of more than one prothrombotic risk factor the incidence rates calculated per 100 patient-years are presented in the table below {table presented}.

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)0.009
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 score0.008
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
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 science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.019
GPT teacher head0.285
Teacher spread0.265 · 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

Citations16
Published2019
Admission routes1
Has abstractyes

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