Nontargeted Native Renal Biopsy Adequacy: Preintervention Data From a Province-Wide, Multicentre, and Interdepartmental Audit
Bibliographic record
Abstract
Background: Nontargeted renal biopsy is essential to diagnosis, classification, and prognostication of medical renal disease. Inadequate biopsies delay diagnosis, expose the patient to repeated biopsy, and increase costs. Objective: The purpose of this project is to characterize nontargeted renal biopsy specimen adequacy and identify areas for improvement. Design: This project was designed as a clinical audit of specimen adequacy rates of nontargeted renal biopsies from 13 hospitals, as well as a questionnaire of radiology and pathology department staff regarding current practices surrounding renal biopsies. Setting: Retrospective analysis of 2188 adult native renal biopsies was performed from January 1, 2018, to September 9, 2021, across 13 hospitals. Patients: Adult patients with medical renal disease undergoing a nontargeted renal biopsy were included. Methods: Retrospective analysis of 2188 adult native renal biopsies was performed from January 1, 2018, to September 9, 2021, across 13 hospitals. Adequacy was divided into 4 categories based on number of glomeruli received: ideally adequate (≥25 glomeruli), minimally adequate (15-24), suboptimal (<15 and diagnosis rendered), and inadequate (<15 and no diagnosis rendered). Two targets were chosen; target 1, to achieve a combined suboptimal and inadequate rate ≤ 10%, and target 2, to attain an ideally adequate rate ≥80%. Radiology department heads in the province were surveyed on biopsy equipment, technique, technologist support, and feasibility of possible interventions to enhance biopsy adequacy. Pathology department staff were surveyed on their education and experience. Results: Adequacy was as follows: ideally adequate 64.7%, minimally adequate 26.0%, suboptimal 7.9%, and inadequate 1.4%. The province (and 8/13 hospitals) met target 1 for native biopsies (9.3%). Two hospitals achieved target 2 for native biopsies. A key finding was that the 2 hospitals with the lowest target 1 scores did not have a technologist present at biopsy. Limitations: Survey data was used to assess biopsy technique at each hospital, and specific technique for each biopsy was not recorded. As such, a multivariate statistical analysis of specimen adequacy rates was not feasible. Data on complications was not collected. Conclusions: Preintervention the province was at target for limiting inadequate and suboptimal native biopsies. There was a substantial shortfall in the ideally adequate rate from the proposed target. Using insight from survey data, interventions with the greatest expected impact were identified and those that are feasible given limited resources will be implemented to improve sample adequacy. Trial Registration: Not registered.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".