Establishing an effective clinical data collecting tool for optimal evaluation of native and allograft renal biopsies
Bibliographic record
Abstract
Introduction: Percutaneous kidney biopsy is the gold standard method to reach a precise diagnosis in most medical kidney diseases, which positively impacts patient care by personalizing the treatment. Accurate diagnosis in the pathology report for medical kidney diseases requires clinicopathological correlation, and clinical data is not always reachable to the nephropathologist. This study aimed to create a standardized, paperless requisition form compatible with medical renal biopsies. Methods: An initial form was prepared for native and allograft renal biopsies according to the current classification of medical kidney diseases. We invited 33 nephropathologists working in Canadian healthcare institutions to answer survey questions about the need to include a particular aspect of clinical information. According to the responses, we modified the experimental form. Eighty nephrologists were asked to complete a clinical data-collecting form given out as PDF files. The time for completing the form and clinicians' satisfaction were assessed. Results: The experimental form survey was answered by 20 out of 33 nephropathologists (61%) from 14 Canadian healthcare centers. The agreement rate on the questions was from 38.89% to 100.00% (average 83.33% and 77.14% for the native and the allograft section, respectively). Seventeen out of 80 nephrologists and their assistants (21%) responded by completing 22 PDF forms. The time required to finish a PDF form was 10.4 min on average. Nephrologists considered the form time-consuming and suggested making it more clinically relevant. Only seven nephrologists responded to the satisfaction survey; four (57%) were satisfied. Conclusions: Medical information is critical in renal pathology diagnoses. A uniform paperless clinical data requisition form was evolved through an agreement by Canadian nephropathologists.
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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 | 0.051 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".