MétaCan
Menu
Back to cohort
Record W3032232988 · doi:10.1016/j.ijsu.2020.05.079

Convalescent plasma therapy in the treatment of COVID-19: Practical considerations: Correspondence

2020· article· en· W3032232988 on OpenAlexaff
Amin Islam, Shafquat Rafiq, Sabina Karim, Ismail Laher, Harunor Rashid

Bibliographic record

VenueInternational Journal of Surgery · 2020
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineCoronavirus disease 2019 (COVID-19)Convalescent plasma2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Coronavirus InfectionsIntensive care medicineBetacoronavirusVirologyInternal medicineDisease

Abstract

fetched live from OpenAlex

It is unclear if convalescent plasma can blunt the development of a natural immune response, especially when used prophylactically. There are multiple clinical trials taking place in several countries including in the UK (e.g., REMAP-CAP and RECOVERY Trials). People who have recovered from COVID-19 for more than 28 days, with no transfusion transmitted diseses and who are not pregnant are eligible to participate. Recipients will be followed throughout their hospitalisation and a month after discharge. While we await trial results, we recommend that CPT be considered for patients severely ill with COVID-19 upon hospitalisation.

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)0.075
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.589
Threshold uncertainty score0.933

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.075
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.327
GPT teacher head0.511
Teacher spread0.184 · 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

Citations20
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of SurgerySame topicCOVID-19 Clinical Research StudiesFrench-language works237,207