Can a Trained Radiology Technician Do Arterial Obstruction Quantification in Patients With Acute Pulmonary Embolism?
Bibliographic record
Abstract
Objectives To assess interobserver variability between a trained radiology technician (RT) and an experienced radiologist in arterial obstruction quantification using the Qanadli obstruction index (QOI), in patients diagnosed with acute pulmonary embolism (APE) at CT pulmonary angiography (CTPA). Materials and Methods A RT and a radiologist independently reviewed CTPAs of 97 consecutive, prospectively enrolled patients with APE, and calculated the QOI. They classified patients into three risk categories: high for QOI ≥40%, intermediate for QOI 20-37.5%, low for QOI <20%. Interobserver variability was investigated for QOI as a continuous variable and as a categorical variable (high, intermediate, and low-risk groups). Results Mean QOI (±SD) was 39.5±24.3% and 38.6±18.9% for the RT and the radiologist, respectively. The mean QOI was not statistically different between the RT and the radiologist (p=0.502), and the interobserver agreement was excellent (ICC=0.905). The RT classified 54 patients (55.7%) as high, 17 (17.53%) as intermediate, and 26 (26.8%) as low risk. The radiologist classified 55 patients (56.7%) as high, 22 (22.7%) as intermediate, and 20 (20.6%) as low risk. The interrater agreement for risk stratification was excellent (weighted kappa=0.844). Conclusion Once the diagnosis of APE was established, an adequately trained RT achieved an accuracy comparable to that of an experienced radiologist regarding QOI calculation and risk assessment.
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 | 0.018 | 0.124 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".