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Record W4396626048 · doi:10.1111/pai.14135

Looking for <scp>ALPS</scp> : The value of a combined assessment of biochemical markers

2024· article· en· W4396626048 on OpenAlexaff
Isabel Fernández, Fabien Touzot

Bibliographic record

VenuePediatric Allergy and Immunology · 2024
Typearticle
Languageen
FieldMedicine
TopicViral-associated cancers and disorders
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineAutoimmune lymphoproliferative syndromeCytopeniaCD8ImmunologyT-cell receptorInternal medicineFas ligandBiomarkerImmune systemApoptosisT cellFas receptor

Abstract

fetched live from OpenAlex

Abstract Background Autoimmune lymphoproliferative syndrome (ALPS) is a rare primary immune disorder caused by defect of the extrinsic apoptotic pathway. The current diagnostic criteria combine clinical features and typical biomarkers but have not been the object of clear international consensus. Methods We conducted a retrospective study on pediatric patients who were investigated for autoimmune cytopenia and/or lymphoproliferation at the CHU Sainte‐Justine Hospital over 10 years. Patients were screened using the combination of TCRαβ + CD4 − CD8 − “double negative” (DN) T cells and soluble plasmatic FAS ligand (sFASL). Results Among the 398 tested patients, the median sFASL and DN T cells were 200 ng/mL and 1.8% of TCRαβ + T cells, respectively. sFASL was highly correlated with vitamin B12 levels. We identified five patients diagnosed with ALPS for whose sFASL and vitamin B12 levels were the more discriminating biomarkers. While ALPS diagnostic criteria had high sensibility, their predictive value remained low. Conclusion sFASL level can efficiently discriminate patients with ALPS when using the appropriate thresholds. Our study highlights the need for an international consensus to redefine the place and threshold of biological biomarkers for ALPS diagnosis.

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 score0.511
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.007
GPT teacher head0.261
Teacher spread0.255 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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