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Record W4386010264 · doi:10.12809/hkmj219260

Congenital central hypoventilation syndrome in children: a Hong Kong perspective

2023· article· en· W4386010264 on OpenAlexaff
KL Hon, Genevieve PG Fung, Alexander K.C. Leung, Karen Ka Yan Leung, Daniel KK Ng

Bibliographic record

VenueHong Kong Medical Journal · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience of respiration and sleep
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
Fundersnot available
KeywordsMedicineCongenital central hypoventilation syndromeHypoventilationPediatricsPerspective (graphical)Internal medicineRespiratory systemArtificial intelligence

Abstract

fetched live from OpenAlex

Congenital central hypoventilation syndrome (CCHS), also known as Ondine's curse, is a central nervous system disorder involving failed autonomic control of breathing.2][3][4] As a sleep-related breathing disorder, CCHS causes ineffective breathing, apnoea, or respiratory arrest during sleep andrarely-wakefulness.The condition can be fatal if untreated.An infant with 'Ondine's curse' (a name based on a Greek myth about a curse that prevented breathing during sleep) was described in 1970. 5The term CCHS was first used in 1962 to describe symptoms in adults, 6 then later to describe symptoms in neonates. 7The current definition of CCHS includes diverse clinical manifestations.Paired-like homeobox 2B (PHOX2B) gene variants are the main causes of the syndrome; other causative variants are rare. 7Early diagnosis can prevent lifethreatening events and long-term sequelae.Better knowledge of CCHS and advances in genetics research will help affected patients to consistently receive early treatment, thereby improving survival and long-term outcomes.This article summarises current management of CCHS in children.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.285
Teacher spread0.261 · 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 source (direct Gemma or distilled Codex), 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
Published2023
Admission routes1
Has abstractyes

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