Does Number of Passes in Native Renal Biopsies Correlate With Core Length?
Bibliographic record
Abstract
Introduction: At our center, the adequacy rate for native renal biopsies was 31% in 2006 based on locally defined numerical criterion. Two subsequent interventions increased the adequacy rate from 31 to 41% and 41% to 81% (P < .0001). Subsequently we studied whether the increase in adequacy rate was related to number of passes. The results showed the increasing trend in adequacy rate was inversely related to number of passes with decreasing trend in average number of cores per case (r = −0.634, r2 = 0.402). Surprisingly, many cases with increased number of passes were suboptimal(SO)/unsatisfactory(U). In this study we questioned (1) whether the number of passes was related to core size; (2) whether our locally defined adequacy criterion was stringent. Methods: We recorded the number of passes (3 vs >3), core length (<1.0 vs >=1cm) and diagnosis for all native renal biopsies reported in three different 3-month periods. Number of passes was correlated with core length for each period for all biopsies (satisfactory [S] + U + SO). Results: The average number of passes in S biopsies was 3.38 compared to 3.63 in SO/U biopsies. 90% SO/U biopsies had at least 2 cores of <1.0 cm length; 76% of all biopsies and 95% of SO/U biopsies with >3 cores had at least one core of <1.0 cm. Other findings were: (1) Diagnosis was not possible in 3 of 55 (9%) satisfactory biopsies and in 5 of 24 (21%) SO/U biopsies; (2) In lupus cases, classification and grading of severity was possible in 6/8 (75%) of ‘S’ biopsies compared to 0/2(0%) of SO/U biopsies. Conclusion: >3 passes had one or more cores of smaller size (<1.0 cm) and therefore, contained lesser glomeruli to evaluate, suggesting that smaller size of the core required more number of passes. Other two findings show our criterion for adequacy was fair in order to evaluate biopsies with focal lesions and for proper classification and grading of the disease.
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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 | 0.005 | 0.021 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".