Accuracy, Reproducibility and User Experience With Standardized Instructions for Measurement of Total Kidney Volume in Autosomal Dominant Polycystic Kidney Disease
Bibliographic record
Abstract
PURPOSE: Total kidney volume (TKV) measurement is integral in clinical management of autosomal dominant polycystic kidney disease (ADPKD) but the gold standard of measurement via stereology/manual planimetry is time-consuming and not readily available to clinicians. This study assessed whether standardized measurement instructions based on an ellipsoid equation enhanced TKV assessment on computed tomographic (CT) images of the kidneys as determined by accuracy, reproducibility, efficiency and/or user acceptability. METHODS: Participating radiologists were randomized to perform TKV measurements with or without standardized instructions. All participants measured the same 3 non-contrast, low-dose CT scans. Accuracy was assessed as variation from TKV measurements obtained by planimetry. Intraclass correlation coefficients and time to complete the measurements were assessed. Surveys assessed prior experience with TKV measurement and user acceptability of the instructions. RESULTS: 49 radiologists participated. There was no difference in accuracy or measurement time between instructed and non-instructed participants. There was a trend towards greater reproducibility with standardized instructions (ICC .8 vs .6). 92% of respondents indicated the instructions were easy to use, 86% agreed the instructions would enhance their comfort with TKV measurement and 75% agreed they would recommend these instructions to colleagues. CONCLUSIONS: Instructed and non-instructed participants demonstrated similar accuracy and time required for TKV measurement, but instructed participants had a trend towards greater reproducibility. There was high acceptability including enhanced user confidence with the instructions. Standardized instructions may be of value for radiologists seeking to improve their confidence in providing clinicians with TKV measurements necessary to appropriately manage this patient population.
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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 | 0.027 | 0.132 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".