MétaCan
Menu
Back to cohort
Record W2009383664 · doi:10.1080/0953710031000137037

Comparison of thrombopoiesis during ITP and HIV-ITP and response to intravenous gammaglobulin treatment

2003· article· en· W2009383664 on OpenAlexaff
Maria I.C. Gyöngyössy‐Issa, James B. Bussel, Cedric J. Carter, Dana V. Devine

Bibliographic record

VenuePlatelets · 2003
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsThrombopoiesisMedicinePlateletThrombopoietinThrombocytopenic purpuraImmunologyImmune thrombocytopeniaInternal medicinePopulationGamma globulinGastroenterologyAntibodyMegakaryocyte

Abstract

fetched live from OpenAlex

Immune thrombocytopenic purpura's diagnosis (ITP) is based on low platelet count and exclusion of clinical conditions rather than a specific diagnostic test. We used the reticulated platelet (RP) assay to study ITP and thrombocytopenia associated with HIV infection (HIV-ITP). Data from 96 ITP and 23 HIV-ITP patients showed low platelet counts (PC) with both high or low %RP suggesting that individuals have different degrees of thrombopoiesis. About 20% of ITP and 46% of HIV-ITP patients had %RP in the 'low' or 'normal' ranges. Grouped by platelet count <30x10(9)/L, 24% ITP and 36% HIV-ITP patients had 'low' to 'normal' %RP. The patient population did not show correlation between PC and %RP, but individuals showed an inverse relationship. Within a week of receiving IVIG, 18 ITP and 9 HIV-ITP patients' PC increased, %RP decreased. Patients with %RP measured within 24 h of IVIG treatment had lower %RP than expected, suggesting dilution by an older platelet population. ITP and HIV-ITP patients' responses to i.v. gammaglobulins were similar. Thrombopoietin levels of ITP patients did not correlate with PC, %RP, or RP count. Estimation of thrombopoiesis by RP assay provides useful information for differentiation among thrombocytopenias.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.046
Threshold uncertainty score0.720

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.022
GPT teacher head0.305
Teacher spread0.283 · 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.

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

Citations4
Published2003
Admission routes1
Has abstractyes

Explore more

Same venuePlateletsSame topicPlatelet Disorders and TreatmentsFrench-language works237,207