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Record W2944813520 · doi:10.1159/000500235

Femoral Neck Fracture in Idiopathic Hypercalciuria with Excessive Cola Consumption: A Case Report

2019· article· en· W2944813520 on OpenAlexaff
Guoju Hong, Xiaorui Han, Wei He, Felix Yao, Jiake Xu, Leilei Chen

Bibliographic record

VenueCase Reports in Orthopedic Research · 2019
Typearticle
Languageen
FieldMedicine
TopicKidney Stones and Urolithiasis Treatments
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China
KeywordsHypercalciuriaMedicineFemoral neckOsteoporosisBone mineralUrologyInternal medicineExcretionPopulationUrineBone remodelingEndocrinologySurgery

Abstract

fetched live from OpenAlex

Idiopathic hypercalciuria is a metabolic defect characterized by excess renal calcium excretion, which can lead to bone mineral loss and an increased propensity to bony fractures. It is more commonly found among Caucasians and is present in the general population with a frequency of 5–10%, but can reach 45–50% in subjects affected by nephrolithiasis. Here we report the case of a young 35-year-old male who developed primary osteoporosis secondary to idiopathic hypercalciuria and sustained a femoral neck fracture after a minor-impact fall. Laboratory findings revealed high urine calcium, low serum potassium, and high serum alkaline phosphatase levels. Low-velocity traumatic bone injury was found in a young patient with hypercalciuria, which may indicate that bone status must be evaluated and followed up in these patients.

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)0.003
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 score0.008
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.003
Science and technology studies0.0040.002
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0080.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.057
GPT teacher head0.395
Teacher spread0.337 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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