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Record W2027342692 · doi:10.1097/mnh.0b013e32835dda01

Bone biopsy in renal osteodystrophy

2013· review· en· W2027342692 on OpenAlexaff
Marta Christov, Renata C. Pereira, Kate Wesseling-Perry

Bibliographic record

VenueCurrent Opinion in Nephrology & Hypertension · 2013
Typereview
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsPediatric Oncology Group
FundersNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsRenal osteodystrophyMedicineBone biopsyBiopsyRenal biopsyPathologyInternal medicineKidney disease

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The pathogenesis and optimal therapy of renal bone disease remains poorly understood in chronic kidney disease (CKD) and dialysis patients. Bone biopsy is thus far the only window into cellular and molecular events in bone. This review will focus on recent insights into the pathophysiology of renal bone disease, as highlighted by bone biopsy, and discuss implications for treatment. RECENT FINDINGS: Abnormalities in bone physiology start very early in children and adults with CKD, when most clinically measurable mineral metabolism parameters are normal. In addition, racial differences, known to exist in serum markers such as parathyroid hormone, also appear prominent in the bone, suggesting that clinical treatment guidelines may not address the needs of all patient populations. The effects of treatments for secondary hyperparathyroidism on bone may be unexpected. SUMMARY: With the help of bone biopsy studies, molecular insights into the pathogenesis of renal osteodystrophy are beginning to emerge. Current therapies may have unexpected effects on bone physiology.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.116
GPT teacher head0.378
Teacher spread0.262 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations9
Published2013
Admission routes1
Has abstractyes

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