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Record W2552037473 · doi:10.1111/hdi.12512

Unusual cause of hypercalcaemia in end stage renal failure patients

2016· article· en· W2552037473 on OpenAlexvenueno aff
Hooi Khee Teo, Jiunn Wong

Bibliographic record

VenueHemodialysis International · 2016
Typearticle
Languageen
FieldMedicine
TopicBone health and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsHypercalcaemiaMedicineCalcitoninDialysisHemodialysisBone remodelingKidney diseaseInternal medicineMetabolic disorderRenal osteodystrophyChronic kidney disease-mineral and bone disorderIntensive care medicineEndocrinologyCalcium

Abstract

fetched live from OpenAlex

Immobility-induced hypercalcaemia is rarely considered in patients on dialysis and is a challenging diagnosis to make. This is especially so due to the lack of biomarkers as well as the notion that calcium metabolism is mostly related to chronic kidney disease-metabolic bone disorder due to the role of iPTH. We present two cases of our dialysis patients, who were clinically unwell from hypercalcemia. We were initially uncertain of the cause of hypercalcemia as despite our attempts to adjust treatment based on their biochemical findings, we were unable to correct the hypercalcemia. We did not have appropriate bone turnover markers to guide us and out of desperation, anti-resorptives-calcitonin and bisphosphonate were given with good clinical response. We concluded that the hypercalcemia was related to immobility-induced hypercalcemia and the inappropriately low iPTH was a red herring. Immobility-induced hypercalcaemia should be considered in patients with end stage renal failure on renal replacement therapy, especially in those with recent and significant immobility. In these patients, pamidronate can be considered should the hypercalcaemia persist.

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)0.002
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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.019
GPT teacher head0.297
Teacher spread0.278 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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