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End points for sickle cell disease clinical trials: renal and cardiopulmonary, cure, and low-resource settings

2019· article· en· W2992870481 on OpenAlexaff
Ann T. Farrell, Julie A. Panepinto, Ankit A. Desai, Adetola A. Kassim, Jeffrey D. Lebensburger, Mark C. Walters, Daniel E. Bauer, Rae Blaylark, Donna DiMichele, Mark T. Gladwin, Nancy Green, Kathryn L. Hassell, Gregory J. Kato, Elizabeth S. Klings, Donald B. Kohn, Lakshmanan Krishnamurti, Jane A. Little, Julie Makani, Punam Malik, Patrick T. McGann, Caterina P. Minniti, Claudia R. Morris, Isaac Odame, Patricia O’Neal, Rosanna Setse, Poornima Sharma, Shalini Shenoy

Bibliographic record

VenueBlood Advances · 2019
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersFeinberg School of MedicineNorthwestern UniversityNational Heart, Lung, and Blood InstituteU.S. Food and Drug AdministrationDoris Duke Charitable Foundation
KeywordsMedicineIntensive care medicineClinical trialFood and drug administrationDiseaseMEDLINEResource (disambiguation)Drug trialPhysical therapyInternal medicineMedical emergency

Abstract

fetched live from OpenAlex

To address the global burden of sickle cell disease and the need for novel therapies, the American Society of Hematology partnered with the US Food and Drug Administration to engage the work of 7 panels of clinicians, investigators, and patients to develop consensus recommendations for clinical trial end points. The panels conducted their work through literature reviews, assessment of available evidence, and expert judgment focusing on end points related to patient-reported outcome, pain (non-patient-reported outcomes), the brain, end-organ considerations, biomarkers, measurement of cure, and low-resource settings. This article presents the findings and recommendations of the end-organ considerations, measurement of cure, and low-resource settings panels as well as relevant findings and recommendations from the biomarkers panel.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.936
Threshold uncertainty score0.517

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.017
GPT teacher head0.319
Teacher spread0.301 · 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 designNot applicable
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

Citations38
Published2019
Admission routes1
Has abstractyes

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