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Record W2094646112 · doi:10.1159/000157877

Impact of Patient Characteristics and Clinical Factors on the Decision to Initiate Growth Hormone Treatment in Turner Syndrome

2008· article· en· W2094646112 on OpenAlexafffund
Karine Khatchadourian, Céline Huot, Nathalie Alos, Guy Van Vliet, Cheri Deal

Bibliographic record

VenueHormone Research in Paediatrics · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and Clinical Aspects of Sex Determination and Chromosomal Abnormalities
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
FundersEli Lilly Canada
KeywordsMedicineTurner syndromeGrowth hormoneBone agePediatricsInternal medicineGrowth hormone treatmentHormoneEndocrinology

Abstract

fetched live from OpenAlex

BACKGROUND/AIMS: To evaluate factors contributing to the decision to initiate treatment with growth hormone (GH) in patients with Turner syndrome (TS). METHODS: Data collected included ethnicity, parents' education and work status, mid-parental height, age at diagnosis, karyotype, pubertal development, clinical severity score, bone age, height SDS and ages when GH was proposed and initiated. RESULTS: GH was proposed to 59 of 72 patients >6 years (82%), and of these 46 (78%) accepted. Reasons for not proposing GH included late diagnosis, good growth and loss to follow-up. GH-treated and untreated girls differed by age at diagnosis (mean +/- SD: 6.8 +/- 4.7 vs. 4.3 +/- 5.1 years; p = 0.04), TS-specific height SDS (0.08 +/- 0.81 vs. 066 +/- 0.87; p = 0.01) and spontaneous puberty (5/46 vs. 4/26, p = 0.024). Mean age at which it was suggested to begin GH was 9.2 +/- 2.9 years. Reasons for parental refusal of GH were not related to reimbursement issues since GH treatment is covered fully by our insurance plan but included concern with other medical issues, mental health problems and fear of injections or unknown side effects. CONCLUSION: GH treatment was not acceptable to all patients with TS.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.387

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 head0.065
GPT teacher head0.369
Teacher spread0.304 · 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 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

Citations6
Published2008
Admission routes2
Has abstractyes

Explore more

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