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Record W2050955327 · doi:10.2147/ijgm.s51302

An unusual case of malignancy-related hypercalcemia

2013· article· en· W2050955327 on OpenAlexafffund
Mary-Anne Doyle, Janine Malcolm

Bibliographic record

VenueInternational Journal of General Medicine · 2013
Typearticle
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsUniversity of Ottawa
FundersLady Davis Institute for Medical ResearchJewish General HospitalMcGill University
KeywordsMedicineMalignancyBioinformaticsPathologyDermatology

Abstract

fetched live from OpenAlex

OBJECTIVE: To report the case of a 28-year-old woman who presented with hypercalcemia (total calcium =4.11 mmol/L), elevated parathyroid hormone (PTH) 24.6 pmol/L, normal parathyroid hormone-related peptide 7.8 pg/mL, and a 63 mm × 57 mm, poorly differentiated neuroendocrine carcinoma (small-cell type) pancreatic mass with liver metastases. INVESTIGATIONS AND TREATMENT: Hypercalcemia was acutely managed with intravenous fluids, pamidronate and calcitonin. Investigations for multiple endocrine neoplasia type 1 and parathyroid adenoma were initiated. The identified neuroendocrine tumor was treated with cisplatinum/etoposide chemotherapy. RESULTS: The pancreatic mass (56 mm × 49 mm) and metastases decreased in size with chemotherapy and calcium levels normalized. Eight months later, calcium increased to 3.23 mmol/L, PTH increased to 48.2 pmol/L, and the pancreatic mass increased in size to 67 mm × 58 mm. The patient was given a trial of cinacalcet but was unable to tolerate it. Chemotherapy was restarted and resulted in a decrease in the pancreatic mass (49 mm × 42 mm), a reduction in PTH levels (16.6 pmol/L), and calcium levels (2.34 mmol/L). CONCLUSION: Ectopic PTH secreting tumors should be considered when there is no parathyroid related cause for an elevated PTH. Recognizing the association between PTH and hypercalcemia of malignancy may lead to an earlier detection of an undiagnosed malignancy.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.550
Threshold uncertainty score0.542

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)0.0000.000

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 head0.015
GPT teacher head0.342
Teacher spread0.327 · 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 designBench or experimental
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

Citations24
Published2013
Admission routes2
Has abstractyes

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