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Record W2039812596 · doi:10.1002/ajmg.a.35415

The Coffin–Siris syndrome: A proposed diagnostic approach and assessment of 15 overlapping cases

2012· article· en· W2039812596 on OpenAlexafffund
Samantha A. Schrier, Joann Bodurtha, Barbara K. Burton, Albert E. Chudley, Mary Anne D. Chiong, M D'Avanzo, Sally Ann Lynch, Antonio Musio, Dmitriy M. Nyazov, Pedro A. Sanchez‐Lara, Stavit A. Shalev, Matthew A. Deardorff

Bibliographic record

VenueAmerican Journal of Medical Genetics Part A · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChromatin Remodeling and Cancer
Canadian institutionsUniversity of Manitoba
FundersNational Institute of General Medical SciencesEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentUniversity of Manitoba
KeywordsCategorizationHypoplasiaMedicinePsychologyComputer scienceArtificial intelligenceSurgery

Abstract

fetched live from OpenAlex

Coffin-Siris syndrome (CSS) is a rare, clinically heterogeneous disorder often considered in the setting of cognitive/developmental delay and 5th finger/nail hypoplasia. Due to the clinical variability of facial and other features, this diagnosis is often difficult to confirm clinically and the existence of this disorder as a specific diagnosis has been at times an issue of debate. In an effort to further delineate the spectrum and key phenotypic features, we reviewed 80 previously reported cases to define features in patients that most closely correlated with a convincing diagnosis. There appear to be two subtypes of CSS, one which displays the "classic" coarse facial features previously described; another displays "variant" facial features which are less striking. Using these features, we defined an algorithm to rank the confidence of diagnosis and applied it to 15 additional patients who had been previously characterized by chromosome microarray. This approach will also facilitate uniform categorization for whole-exome analysis.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.744
Threshold uncertainty score0.293

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.017
GPT teacher head0.313
Teacher spread0.296 · 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 designOther design
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

Citations91
Published2012
Admission routes2
Has abstractyes

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