Lost in Transportation: Calcium Oxalate Crystals in Kidney Biopsy Specimens Fixed in Michel Medium May Disappear
Bibliographic record
Abstract
Context.— Calcium oxalate (CaOx) deposits in a kidney biopsy specimen can be seen in acute or chronic kidney injury and in oxalate nephropathy. Although no established cutoff criteria to diagnose oxalate nephropathy versus incidental CaOx deposition in the kidney exist, these conditions require different treatment. We noticed a significant decrease in the number of CaOx deposits in the kidney biopsy cores that were fixed in Michel transport medium (MTM) as compared to their counterparts fixed in formalin. Objective.— To investigate the impact of different fixatives on the number of CaOx deposits in kidney biopsy specimens. Design.— Retrospective search for kidney biopsies with diagnosis of CaOx deposition was performed in our Renal Pathology Database between January 1, 2015 and October 15, 2018. Results.— Seventy-six biopsies with an increased number of CaOx deposits were identified. CaOx deposits were counted on slides from the frozen tissue (MTM fixed or fresh frozen) and from the formalin-fixed cores. The density of CaOx deposits was significantly higher in formalin-fixed cores (13.6 ± 10.0/cm) than in MTM-fixed cores (3.2 ± 5.1/cm; P < .001). CaOx density in the kidney biopsy specimens decreased progressively with increased fixation time in MTM. No significant differences in the CaOx density between formalin-fixed and fresh frozen tissue were observed. Conclusions.— Our data demonstrate that fixation in MTM may result in a significant reduction in the number of CaOx deposits in a kidney biopsy specimen. This may make the diagnosis difficult, especially in small biopsy specimens with limited tissue in the formalin-fixed paraffin block.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".