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The North American Immune Tolerance Registry: contributions to the thirty‐year experience with immune tolerance therapy

2009· article· en· W2118580564 on OpenAlexaboutno aff
Donna DiMichele

Bibliographic record

VenueHaemophilia · 2009
Typearticle
Languageen
FieldMedicine
TopicHemophilia Treatment and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHaemophiliaHaemophilia AClinical trialIntensive care medicineImmunologyFamily medicinePediatricsInternal medicine

Abstract

fetched live from OpenAlex

The North American Immune Tolerance Registry (NAITR) began in 1992 as a project of the ISTH Factor VIII/IX Subcommittee with the goal of further determining immune tolerance induction (ITI) practices in Canada and the United States. This retrospective registry study, published in 2002, was limited in its capacity to provide definitive answers to many unresolved ITI practice issues. Nonetheless, it played a role in developing guidelines for current ITI practice and in generating hypotheses that must now be examined through rigorous prospective data collection efforts. For haemophilia A, the logical next step has been the initiation of international prospective randomized studies of ITI outcome relative to factor VIII (FVIII) dose and purity for subjects with high titre inhibitors. Both trials will additionally provide platforms for translational study of the immunology of tolerance, a prelude to the next generation of safe and effective tolerizing strategies. For the less common problem of FIX inhibitor eradication, prospective randomized studies will not be a feasible way to confirm the NAITR observations. Coordinated international efforts will still be required to prospectively collect data on ITI outcome to document new potentially effective therapeutic strategies for inhibitor eradication. These registries will hopefully also serve to identify potential subjects for scientific studies of immunology of haemophilia B-related allergic phenomena, a devastating complication of FIX antibody development.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.582
Threshold uncertainty score0.794

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.016
GPT teacher head0.309
Teacher spread0.293 · 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

Citations92
Published2009
Admission routes1
Has abstractyes

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