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Record W2240519586 · doi:10.1515/jpem.2000.13.7.879

Diagnosis and Management of Juvenile Hyperthyroidism in Germany: A Retrospective Multicenter Study

2000· article· en· W2240519586 on OpenAlexfundno aff
Jörg Dötsch, Thomas Siebler, B. P. Hauffa, Beate Doeker, Werner Andler, Markus Bettendorf, U. Heinrich, Bettina Gohlke, Norbert Albers, H Willgerodt

Bibliographic record

VenueJournal of Pediatric Endocrinology and Metabolism · 2000
Typearticle
Languageen
FieldMedicine
TopicThyroid Disorders and Treatments
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsMedicineCarbimazolePediatricsDiscontinuationPropylthiouracilAntithyroid drugsRetrospective cohort studyMulticenter studyThyroidectomyAntithyroid agentInternal medicineGraves' diseaseThyroidRandomized controlled trial

Abstract

fetched live from OpenAlex

This retrospective multicenter study was designed to survey the management of childhood and adolescent hyperthyroidism in six pediatric endocrinological units in Germany. Fifty-six patients aged between 1.1 and 17.0 yr (median 10.5 yr) were enrolled. Data were collected retrospectively from the patients' records by a trained pediatric endocrinologist using standardized questionnaires. After the diagnosis of hyperthyroidism was established on the basis of clinical and biological findings, treatment with antithyroid drugs (carbimazole, methimazole, thiamazole, propylthiouracil) was started in all patients. In 55/56 of the patients treated with antithyroid drugs, euthyroidism was achieved (98%). However, 26 patients (47%) were still hyperthyroid after discontinuation of the medication. Eight children with continued hyperthyroidism ultimately underwent subtotal thyroidectomy 13-136 (median 28) months after the initial diagnosis. Management principles of the participating centers were heterogeneous. As a consequence, prospective multicenter studies are urgently needed to establish clear standards for the diagnosis and therapy of childhood hyperthyroidism.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.391

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.011
GPT teacher head0.271
Teacher spread0.260 · 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

Citations47
Published2000
Admission routes1
Has abstractyes

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