The role of electron microscopy in kidney lesions: A review of its diagnostic importance
Bibliographic record
Abstract
Electron microscopy is a technology which was at one time widely used for renal as well as non-renal benign and malignant diseases, but its use has been rapidly declining as hospitals all over the world cut down on expenses. This leaves the renal pathologist with only light microscopy and immunofl uorescence at his disposal to diagnose diseases. Few studies have stated the importance of electron microscopy in routine renal biopsy reporting. We look at different cases where electron microscopy has been helpful in diagnosis and review the literature to assess the role this investigative modality still has to play in modern renal pathology. Journal of Pathology of Nepal (2013) Vol. 3, No.1, Issue 5, 411-415 DOI: http://dx.doi.org/10.3126/jpn.v3i5.7871
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".