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Record W4378635638 · doi:10.14785/lymphosign-2023-0005

Management of newborn screening for severe combined immunodeficiency at a quaternary referral centre—an updated algorithm

2023· article· en· W4378635638 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueLymphoSign Journal · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunodeficiency and Autoimmune Disorders
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsReferralPediatricsSevere combined immunodeficiencyNewborn screeningMedicineIntervention (counseling)AlgorithmFamily medicineBiologyComputer science

Abstract

fetched live from OpenAlex

Severe combined immunodeficiency (SCID) is caused by critical genetic defects affecting the immune system. Early diagnosis and intervention are essential for preventing life-threatening infections, end-organ damage, and complications. Newborn screening for SCID is currently performed in many provinces and territories across Canada. The SickKids Newborn Screening Centre in Toronto, Ontario, is a quaternary referral centre that has evaluated SCID newborn screen-positive infants since the program’s introduction in 2013. Here, we provide updated algorithms for clinical investigation and follow-up of infants with an initial positive screen. Statement of novelty: We provide algorithms for the clinical follow-up of SCID newborn screen-positive infants at a quaternary referral centre.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.762
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.257
Teacher spread0.237 · 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