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Record W3007897069 · doi:10.1089/thy.2019.0807

The Thyrotropin Receptor Mutation Database Update

2020· article· en· W3007897069 on OpenAlex
Alexandra Stephenson, Lorraine Lau, Markus Eszlinger, Ralf Paschke

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThyroid · 2020
Typearticle
Languageen
FieldMedicine
TopicThyroid Disorders and Treatments
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMutationDatabaseThyrotropin receptorFunction (biology)GeneticsMedicineBiologyComputational biologyBioinformaticsComputer scienceGeneThyroidGraves' disease

Abstract

fetched live from OpenAlex

The thyrotropin receptor ( TSHR ) mutation database, consisting of all known TSHR mutations and their clinical characterizations, was established in 1999. The database contents are updated here with the same website ( tsh-receptor-mutation-database.org ). The new database contains 638 cases of TSHR mutations: 448 cases of gain of function mutations (7 novel mutations and 41 new cases for previously described mutations since its last update in 2012) and 190 cases of loss of function mutations (28 novel mutations and 31 new cases for previously described mutations since its last update in 2012). This database is continuously updated and allows for rapid validation of patient TSHR mutations causing hyper- or hypothyroidism or insensitivity to TSH.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.002

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 head0.018
GPT teacher head0.261
Teacher spread0.244 · 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