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Record W4401015164 · doi:10.1016/j.cpccr.2024.100317

Challenges in managing extensive brown tumors and renal stones in a young man with parathyroid carcinoma and single kidney: Case report

2024· article· en· W4401015164 on OpenAlexaff
Syeda Sara Tajammul, Syed Furqan Hashmi, Zamzam Al Hashami, Laila Al Masaoudi, Sharjeel Usmani, Asma Naz Nadaf, Layth Mula‐Hussain

Bibliographic record

VenueCurrent Problems in Cancer Case Reports · 2024
Typearticle
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsDalhousie University
Fundersnot available
KeywordsParathyroid carcinomaMedicinePrimary hyperparathyroidismStage (stratigraphy)Radiation therapyHyperparathyroidismRadiologySurgery

Abstract

fetched live from OpenAlex

Parathyroid carcinoma is one of the rare causes of primary hyperparathyroidism, comprising less than 1 % of its cases. Diagnosing parathyroid carcinoma typically requires a comprehensive evaluation using clinical, histological, and radiological methods. Primary hyperparathyroidism secondary to parathyroid carcinoma often presents with hypercalcemia, bone abnormalities, and renal stones. Brown tumours, a rare and late manifestation of hyperparathyroidism, signify the final stage in bone remodelling, and they can be easily mistaken for bony metastases, which highlights the importance of distinguishing between them to ensure appropriate management and avoid unnecessary treatment. Due to the rarity of parathyroid carcinoma, there is currently no standardized staging system or specific guidelines for its management. Consequently, the involvement of a multidisciplinary team has become crucial in addressing this disease. Surgery is considered the primary treatment approach, while the role of adjuvant radiotherapy and chemotherapy remains controversial. With ongoing research, the treatment landscape for parathyroid carcinoma may evolve, offering new hope for improved outcomes and quality of life for affected individuals.

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 categoriesMeta-epidemiology (narrow)
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 score0.265
Threshold uncertainty score1.000

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 head0.062
GPT teacher head0.328
Teacher spread0.267 · 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.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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