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Record W2963237283 · doi:10.7861/clinmedicine.19-3-s82

A HaemSTAR-led, UK-wide ‘flash-mob’ audit of intravenous immunoglobulin use in immune thrombocytopenia

2019· article· en· W2963237283 on OpenAlexaff
Phillip L.R. Nicolson, Rita Perry, Amelia Fisher, Gemma Scott, Laura Magill, D. Chan‐Lam, Alice Thorpe, Mac Macheta, Luke Carter‐Brzezinski, Sam Ackroyd, Alvin Katumba, Charlotte Bradbury, Sheila Jen, Marquita Camillieri, Martin Besser, Tom Bull, Katherine Leighton, Yezenash Ayalew, John Willan, Edmund Watson, Pamela Oshinyemi, Yogesh Upadhye, Keir Pickard, Imogen Swart-Rimmer, Chloe Knott, Sally Chown, Francesca Crolla, Daire Quinn, Lindsay McLeod-Kennedy, Hajer Oun, Christopher McDermott, Mairi Walker, Ryan Mullally, Naoimh Herlihy, Gulnaz Shah, Andrew J. Doyle, Susan Robinson, Zara Sayar, Rebecca Pryor, Chris Peet, Amir Shenouda, Indrani Venkatadasari, Jorge Cartier, Melek Akay, Dimitris A. Tsitsikas, Suthesh Sivapalaratnam, Nichola Cooper, Claire Lentaigne, Chris Bailey, Dan Mei Xu, Sine Janum, Arunodaya Mohan, Katja Kimberger, Maipelo Kgologolo, Belen Sevillano, Sophie Hanina, Akila Danga, Chira Mustafa, Charlotte Wilding, Roochi Trikha, Han Wang, Cristina Crossette-Thambiah, Andrew K. Hastings, Sree Sreedhara, David W. Wright, Laura Batey, Abigail Atkin, Sarah Davis, Sarah Jaafar, Ayesha Ejaz, Tina Biss, Jennifer Swieton, Mohd Sharin Mohd Noh, Holly Gibson, Tanya Freeman, Upekha Badaguma, Olivia Kreze, Suriya Kirkpatrick, Surenthini Suntharalingam, Miroslab Kmonicek, Michael Joffe, Dan Halperin, Michael Desborough, Alexandros Rampotas, Elissa Dhillon, Paul Greaves, Edward Blacker, Laura Aiken, Jesca Boot, Nithya Prasannan, Jonathan P. Kerr, Abi Martin, Sarah Wexler, Claire Burney, Michelle Melly, Regina Nolan, Rupert Hipkins, Israa Kaddam, Shereef Elmoamly, Jennifer Darlow, Dianne Plews, Caroline Shrubsole, Eleana Loizou, Louise Garth, Hina Peter, Julia Wolf, Shivali Walia, Vickie MacDonald, Abbas Zaidi, Robert Dunk, Haroon Miah, Atiqa Miah, David Tucker, Thomas Skinner, Seda Cakmak, Ipek Cakmak, Hayder Hussein, Richard J. Buka, Lydia Wilson, Georgina Talbot, Hafiz Qureshi, Sarah Wharin, Anna Dillon, Benjamin Bailiff, Graham McIlroy, Duncan Murray, Frances Seymour, Jane Graham, Samuel Harrison, Beena Salhan, David M. Sharpe, Wayne R. Thomas, Rory McCulloch, Nicola Crosbie, Gillian Lowe, Quentin A. Hill

Bibliographic record

VenueClinical Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsRoyal Ottawa Mental Health Centre
Fundersnot available
KeywordsMedicineIntravenous ImmunoglobulinsAntibodyImmune systemIntravenous Immunoglobulin TherapyImmune thrombocytopeniaImmunologyIntravenous InfusionsInternal medicine

Abstract

fetched live from OpenAlex

Intravenous immunoglobulin (IVIg) is a common therapy for patients with immune thrombocytopenia (ITP). The initial response rate for IVIg is 80%[1][1] and is typically rapid, with some patients responding in 24 hours, although usually in 2–4 days.[2][2] When IVIg is used alone, the response is

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.039
GPT teacher head0.348
Teacher spread0.309 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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