Division of an Iliac Crest Bone Biopsy Specimen to Allow Histomorphometry, Immunohistochemical, Molecular Analysis, and Tissue Banking: Technical Aspect and Applications
Bibliographic record
Abstract
ABSTRACT The evaluation of bone complications in chronic kidney disease (CKD) often requires a bone biopsy, the analysis of which can refine the diagnosis of bone defects. Bone histomorphometry performed on sections of the iliac crest biopsy remains the reference procedure for the quantitative assessment of bone health in CKD patients, whereas immunohistochemistry and other molecular biology analyses are indispensable tools for studying the disrupted signaling pathways. Traditionally, the whole iliac crest biopsy was included in methyl‐methacrylate (MMA) and was exclusively used for bone histomorphometry to describe static, dynamic, and structural parameters. Therefore, further molecular analysis of the bone tissue or the need for tissue banking would require a second biopsy to be made, because inclusion in MMA prevents the extraction of good‐quality nucleic acids. In this work, we describe a simple approach to divide a single iliac crest bone biopsy into multiple parts. This allows for simultaneous assessments of histology, immunohistochemistry, biomolecular analysis, and tissue banking while preserving the same bone surface area for histomorphometry. © 2020 American Society for Bone and Mineral Research © 2020 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".