Coronary artery calcification scores in patients with chronic kidney disease prior to dialysis: reliability as a trial outcome measure
Bibliographic record
Abstract
BACKGROUND: Coronary artery calcification (CAC) is prevalent in patients with chronic kidney disease (CKD). Data on the reliability and validity of high-resolution computerized tomography (HRCT) in patients with CKD is lacking. The purpose of this study was to evaluate the inter- and intra-reviewer agreement and inter-scan reproducibility of CACS measurement with HRCT in a cohort of patients with CKD prior to dialysis, and to compare the change in CACS at 30 minutes to the change in CACS over 1 year. METHODS: Thirty-three patients with CKD not yet on dialysis underwent an HRCT scan at baseline and 1 year to assess for CAC and CAC progression. Two radiologists independently reviewed films and each radiologist re-reviewed a randomly selected subset of films they had previously viewed, to assess for inter-reviewer and intra-reviewer reliability, respectively. Patients underwent a repeat scan within 30 min of the first baseline scan to assess for inter-scan reproducibility. RESULTS: At baseline, eight patients (24%) had no CAC. Of the 25 patients (76%) with CAC, 10 (40%) had severe calcification. Intra-reviewer agreement was 83%. Inter-reviewer agreement ranged between 77 and 94%. Six (27%) of the patients with >30 baseline CACS had >15% change in CACS following repositioning. Four of these patients had an increase in CACS with position change [18% (95% CI: 5-40%)]. Of the 21 patients who underwent a follow-up scan at 1 year, 7 (33%) demonstrated CACS progression. CONCLUSIONS: There is significant imprecision in HRCT-derived CACS in CKD patients. This suggests a need for standardization of methods of CACS measurement with HRCT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".