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Record W2147787502 · doi:10.1002/hed.22918

Hyperparathyroidism–jaw tumor syndrome

2012· article· en· W2147787502 on OpenAlexaff
Matthew Kutcher, Matthew H. Rigby, Martin Bullock, Jonathan Trites, S. Mark Taylor, Robert D. Hart

Bibliographic record

VenueHead & Neck · 2012
Typearticle
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineHyperparathyroidismParathyroidectomyParathyroid carcinomaVomitingSurgeryPrimary hyperparathyroidismAnemiaParathyroid glandNauseaGastroenterologyInternal medicineParathyroid hormoneCalcium

Abstract

fetched live from OpenAlex

BACKGROUND: Hyperparathyroidism-jaw tumor (HPT-JT) syndrome is a rare autosomal dominant multiple tumor syndrome characterized by hyperparathyroidism due to single or multiple-gland parathyroid tumor(s). Since it was first described in 1990, the genetics underlying the syndrome have been elucidated and typical clinical presentations are becoming clarified as literature describing this rare entity amasses. METHODS AND RESULTS: A 22-year-old man presented with a 2-year history of fatigue, weight loss, nausea, and vomiting. Anemia workup indicated severe hypercalcemia. Investigations were consistent with a diagnosis of HPT-JT. The patient underwent a total 4-gland parathyroidectomy with single gland reimplantation. CONCLUSION: HPT-JT is a complex syndrome with phenotypic manifestations that can seem physiologically and temporally unrelated. The risk of parathyroid carcinoma is elevated in patients with HPT-JT, necessitating rapid treatment and complete tumor resection to reduce the morbidity and mortality associated with intractable hypercalcemia due to local recurrence or metastatic disease.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.027
GPT teacher head0.307
Teacher spread0.279 · 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 designCase report
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

Citations40
Published2012
Admission routes1
Has abstractyes

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