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Record W4388601535 · doi:10.1016/j.rpth.2023.100360

OC 05.2 Impact of Heparin on Disease State Transition in Hospitalized, Non-Critically Ill Patients with COVID-19; Secondary Analysis of an International Multi-Platform RCT

2023· article· en· W4388601535 on OpenAlexaff
Quinn Tays, Brett L. Houston, Weiping Teng, Robert Balshaw, Patrick R. Lawler, S. Lother, Allan Garland, Asher A. Mendelson, Emily Rimmer, Donald S. Houston, Matthew D. Neal, Michael E. Farkouh, Marc Carrier, Ewan C. Goligher, Lana A. Castellucci, Robert E. Ariano, Jamie Falk, Anna Heath, Ryan Zarychanski

Bibliographic record

VenueResearch and Practice in Thrombosis and Haemostasis · 2023
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsOttawa HospitalUniversity of TorontoUniversity of OttawaHospital for Sick ChildrenUniversity of Manitoba
Fundersnot available
KeywordsCritically illCoronavirus disease 2019 (COVID-19)Randomized controlled trialMedicineIntensive care medicineHeparinState (computer science)Internal medicineDiseaseComputer scienceInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Conclusion(s): COVID-19 platelets show a strikingly defective NO-production, independent from disease severity, while platelets from non-COVID-19-ICU generate NO normally, showing a selective SARS-CoV-2 infectionrelated defect of platelet NO-production.We also show an increase in ROSproduction by COVID-19 platelets, and an alteration present however also in non-COVID-19-ICU patients and probably related to critical illness.Platelets may play a role in the previously reported decreased NO-bioavailability and oxidative stress in COVID-19.

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)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.156
GPT teacher head0.534
Teacher spread0.378 · 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 designMeta-analysis
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

Citations2
Published2023
Admission routes1
Has abstractyes

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