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Record W1983611341 · doi:10.1002/ajmg.a.35704

Special section. Syndrome‐specific growth charts

2012· article· en· W1983611341 on OpenAlexaff
Judith G. Hall, Judith Allanson, Karen W. Gripp, Anne Slavotinek

Bibliographic record

VenueAmerican Journal of Medical Genetics Part A · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Syndromes and Imprinting
Canadian institutionsBC Children's HospitalChildren's Hospital of Eastern OntarioUniversity of British Columbia
FundersBarth Syndrome Foundation
KeywordsGeneticistTerminologyIdentification (biology)NormativeDiseaseMedical geneticsPsychologyMedicineBiologyGeneticsPathologyPolitical science

Abstract

fetched live from OpenAlex

Interestingly, new data from around the world suggest that the health and nutrient status of the mother and breast feeding influence growth in addition to ethnicity and other genetic and environmental factors [WHO, 2006]. One of the greatest challenges for the clinical geneticist, when seeing a family, is to provide natural history information for the particular disorder, syndrome, or disease. As geneticists become skilled at describing physical features, improved terminology, and better measurements are becoming available for the clinical setting [Allanson et al., 2009]. As developmental biologists begin to identify the multiple genes involved in growth and development, better understanding of the changes leading to abnormal growth will likely be identified. Nevertheless, the physician, the affected individual, and the family will continue to seek “normal” or normative curves for these disorders since they are a key part of the ongoing assessment of individuals with a particular condition. This Special Section of the Journal is aimed at increasing awareness of the importance of having comprehensive growth information for each of the many disorders in clinical genetics. The handbook that we have edited over the last 20 years has been improving with every edition [Hall et al., 1989; Hall et al., 2007]. The articles in this issue of the American Journal of Medical Genetics will make it even more useful since they characterize the growth patterns in specific conditions and, often, in specific populations. With the advances in the understanding of developmental genetics, identification of specific genes and pathways will allow for more and more specific therapies for rare disorders. As therapies for these syndromes become available, it will be important to be able to compare those treated with a population without therapy (see El-Bassyouni et al. regarding Turner syndrome in this issue). The study of growth in the general population is remarkably complex and involves multiple modifiers. Little wonder then that it has been challenging to accumulate data on rare disorders. This Special Section in this issue of the Journal comprises 14 articles, most providing normative growth curves for various syndromes; some of the articles providing ethnicity-specific growth curves. The articles report on 10 different syndromes, all well known to the practicing pediatrician, clinical geneticist, and genetic counselor. The opening two articles feature growth curves in children with Down syndrome (DS). The article by Affifi et al. includes growth charts of children in the first 3 years of life; the data come from children without the common medical complications of DS, including structural heart defects. Curves for weight, length, and occipito-frontal-circumference (OFC) will be useful in the evaluation of infants with DS. The article by Beyhan et al. presents growth curves of individuals with DS between birth and age 18 years. Associated major malformations were recorded. Both articles lay the groundwork for the upcoming growth curves in U.S. children with DS being compiled by the Centers for Disease Control and Prevention. The next three articles provide growth curves for individuals with the 22q11.2 deletion syndrome; the first two by Tarquinio et al. and Habel and colleagues are derived from United States data, while the article by Guzman et al. presents data derived from Chilean infants with the syndrome from birth to age 24 months. Bassyouni et al. summarize anthropometric measurements in 93 patients with full 45,X or 45,X mosaic karyotypes. None of the individuals has been treated with growth hormone, thus these measurements provide a valuable resource for future comparison of populations of treated and untreated girls with Turner syndrome. The next two articles feature growth curves for individuals with disorders in the RAS/MAPK pathway. The study by Sammon et al. presents normative growth curves for individuals with Costello syndrome at various ages. Measurements obtained about? Do you mean after growth hormone exposure in a small number of the studied individuals were excluded from the analysis. Given the notable manifestation of growth deficiency in infants with Costello syndrome, we anticipate that these data will constitute an important resource. Malaquis et al. provide growth standards for patients with Noonan syndrome and so-called “Noonan-like syndromes” who have mutations in the RAS/MAPK pathway. As height in individuals with Noonan syndrome is correlated to the specific affected gene in this heterogeneous condition, future work may focus on comparing and contrasting cohorts stratified by mutated gene. Lee et al. present growth charts for individuals with Smith–Lemli–Opitz syndrome. These collaborators provide longitudinal data from 78 patients from near birth to age 16 years. Their study represents the largest cohort available of anthropometric data on this important syndrome. The timing of this is particularly relevant, as the upcoming issue in the American Journal of Medical Genetics Part C, Seminars in Medical Genetics, is devoted to Smith–Lemli–Opitz and related syndromes. The next two articles deal with anthropometric measurements in two skeletal dysplasias familiar to the clinical geneticist: osteogenesis imperfecta subtypes (not sure if this is best, but not sure also about The osteogenesis imperfectas) and the Majewski osteodysplastic primordial dwarfism type 2 syndrome (MOPD 2). In MOPD 2, length, weight, and OFC curves for a relatively large series of pericentrin (PCNT) mutation positive patients with this rare condition are provided. The curves will be helpful in the diagnosis of this complex condition. The ensuing two articles concern important but less common conditions, Barth syndrome and Meier-Gorlin syndrome, and provide useful clinical details for these conditions beyond the growth curves. Roberts et al. present data from the Barth syndrome registry and provide growth curves for weight and length compared to the normative population; other clinical data derived from the registry, including developmental milestones and age of diagnosis of cardiomyopathy, are provided. The article by de Munnik and colleagues presents a large series of patients with Meier-Gorlin syndrome, providing growth data, as well as information on secondary sexual development. These data will be invaluable for clinicians of all specialties following children with these two rare syndromes. The final article in the series is unique. Rosenbloom and Butler describe the experiences at two medical centers in different stages of incorporating electronic growth charts into medical records using Prader–Willi syndrome as an illustration. A recent article in the pediatric literature from this group [Butler et al., 2011] provides updated growth charts in individuals with PWS. In addition to the articles in this Special Section, Boghossian et al. [2012] submitted their article on growth curves in trisomy 21, 18, and 13 earlier in 2011. The article was meant to be included in this Special Section but was published before our series. This article, along with all 14 articles summarized above, will appear together in a Virtual Issue of the American Journal of Medical Genetics Part A, which will be online and freely accessible in 2013 on the Wiley Blackwell Interscience web page of the AJMG at http://www.interscience.wiley.com/ajmg. In this way, all of these charts are available for printing or electronic inclusion into patients' charts. The authors appreciate the assistance of John C. Carey in the finalization of the manuscript.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.565
Threshold uncertainty score0.650

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 studies0.0000.000
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 head0.012
GPT teacher head0.253
Teacher spread0.241 · 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 designNot applicable
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".

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Citations11
Published2012
Admission routes1
Has abstractyes

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