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Record W2334041571 · doi:10.1055/s-2006-945793

ANALYSIS OF CLINICAL FEATURES PREDICTING ETIOLOGIC YIELD IN THE ASSESSMENT OF GLOBAL DEVELOPMENTAL DELAY

2006· article· en· W2334041571 on OpenAlexaff
Myriam Srour, Barbara Mazer, Michael Shevell

Bibliographic record

VenueNeuropediatrics · 2006
Typearticle
Languageen
FieldMedicine
TopicNeonatal and fetal brain pathology
Canadian institutionsMcGill UniversityMontreal Children's Hospital
Fundersnot available
KeywordsGlobal developmental delayMedicineIdentification (biology)Presentation (obstetrics)Developmental ageYield (engineering)PediatricsDevelopmental psychologyPhenotypeSurgeryPsychologyGeneticsBiology

Abstract

fetched live from OpenAlex

Objectives: Global developmental delay is a common reason for presentation for neurologic evaluation. However the optimal diagnostic work-up for developmental delay has not yet been precisely defined. This study examined the role of clinical features in predicting the identification of an underlying cause for a child's global developmental delay. Methods: Over a 10-year inclusive interval, the case records of all consecutive children less than 5 years of age referred to a single ambulatory practice setting for global developmental delay were systematically reviewed. The utility of clinical features in predicting the identification of a specific underlying cause for a child's delay was tested using χ 2 -square analysis. Results: A total of 261 patients eventually met criteria for study inclusion. Mean age at initial evaluation was 33.6 months. An underlying cause was found in 98 children (38%). Commonest etiologic groupings were; genetic syndrome/chromosomal abnormality, intrapartum asphyxia, cerebral dysgenesis, psychosocial deprivation and toxin exposure. Factors associated with the ability to eventually identify an underlying cause included; female gender (40/68 [59%] vs. 58/193 [30%], χ 2 =17.8, p<0.001), abnormal prenatal/perinatal history (52/85 [61%] vs. 46/176 [26%], χ 2 =30.0, p<0.001), absence of autistic features (85/159 [54%] vs. 13/102 [13%], χ 2 =43.9, p<0.001), presence of microcephaly (26/40 [65%] vs. 72/221 [32%], χ 2 =15.2, p<0.001), abnormal neurological examination (52/71 [73%] vs. 46/190 [24%], χ 2 =53.0, p<0.001) and dysmorphic features (44/84 [52%] vs. 54/177 [31%], χ 2 =11.6, p=0.001). In 113 children without any abnormal features identified on history or physical examination, routine screening investigations (karyotype, FMR1 and neuroimaging) revealed an underlying etiology in 18 (16%). Conclusion: Etiologic yield in an unselected series of young children with global developmental delay is close to 40% overall and 55% in the absence of any co-existing autistic features. Clinical features are readily apparent that may enhance an expectation of a successful etiologic search. Even in the absence of these clinical features, screening investigations may yield an underlying cause and thus should be routinely undertaken.

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)0.002
metaresearch head score (Gemma)0.017
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 score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.036
GPT teacher head0.350
Teacher spread0.313 · 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
Published2006
Admission routes1
Has abstractyes

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