Radiological Society of North America (RSNA) Expert Consensus Statement Related to Chest CT Findings in COVID-19 Versus CO-RADS: Comparison of Reporting System Performance Among Chest Radiologists and End-User Preference
Bibliographic record
Abstract
Purpose: The RSNA expert consensus statement and CO-RADS reporting system assist radiologists in describing lung imaging findings in a standardized manner in patients under investigation for COVID-19 pneumonia and provide clarity in communication with other healthcare providers. We aim to compare diagnostic performance and inter-/intra-observer among chest radiologists in the interpretation of RSNA and CO-RADS reporting systems and assess clinician preference. Methods: Chest CT scans of 279 patients with suspected COVID-19 who underwent RT-PCR testing were retrospectively and independently examined by 3 chest radiologists who assigned interpretation according to the RSNA and CO-RADS reporting systems. Inter-/intra-observer analysis was performed. Diagnostic accuracy of both reporting systems was calculated. 60 clinicians participated in a survey to assess end-user preference of the reporting systems. Results: Both systems demonstrated almost perfect inter-observer agreement (Fleiss kappa 0.871, P < 0.0001 for RSNA; 0.876, P < 0.0001 for CO-RADS impressions). Intra-observer agreement between the 2 scoring systems using the equivalent categories was almost perfect (Fleiss kappa 0.90-0.92, P < 0.001). Positive predictive values were high, 0.798-0.818 for RSNA and 0.891-0.903 CO-RADS. Negative predictive value were similar, 0.573-0.585 for RSNA and 0.573-0.58 for CO-RADS. Specificity differed between the 2 systems, 68-73% for CO-RADS and 52-58% for RSNA with superior specificity of CO-RADS. Of 60 survey participants, the majority preferred the RSNA reporting system rather than CO-RADS for all options provided (66.7-76.7%; P < 0.05). Conclusions: RSNA and CO-RADS reporting systems are consistent and reproducible with near perfect inter-/intra-observer agreement and excellent positive predictive value. End-users preferred the reporting language in the RSNA system.
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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 | 0.063 | 0.140 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".