Does training and experience influence the accuracy of computed tomography colonography interpretation?
Bibliographic record
Abstract
AIM: To evaluate the effect of experience on the accuracy rate of computed tomography colonography (CTC) interpretation and patient preferences/satisfaction for CTC and colonoscopy. METHODS: A prospective, non-randomized, observational study performed in a single, tertiary care center involving 90 adults who underwent CTC followed by colonoscopy on the same day. CTC was interpreted by an abdominal imaging radiologist and then a colonoscopy was performed utilizing segmental un-blinding and re-examination as required. A radiology resident and two gastroenterology (GI) fellows blinded to the results also interpreted the CTC datasets independently. Accuracy rates and trend changes were determined for each reader to assess for a learning curve. RESULTS: Among 90 patients (57% male) aged 55 ± 8.9 years, 39 polyps ≥ 6 mm were detected in 20 patients and 13 polyps > 9 mm in 10 patients. Accuracy rates were 88.9% (≥ 6 mm) and 93.3% (> 9 mm) for the GI Radiologist, 89.8% (≥ 6 mm) and 98.9% (> 9 mm) for the Radiology Resident and 86.7% and 95.6% (≥ 6 mm) and 87.8% and 94.4% (> 9 mm) for each of the GI fellows respectively. The reader's accuracy rate did not change significantly with the percentage change rate ranging between -1.7 to 0.9 (P = 0.12 to 0.56). Patients considered colonoscopy more satisfactory than CTC (30% vs 4%, P < 0.0001), they felt less anxiety during colonoscopy (36% vs 7%, P < 0.0001), they experienced less pain or discomfort during colonoscopy compared to CTC (69% vs 4%, P < 0.0001) and colonoscopy was preferred by 77% of the participants as a repeat screening test for the future. CONCLUSION: No statistically significant learning curve was identified in CTC interpretation suggesting that further study is required to identify the necessary training to adequately interpret CTC scans.
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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 | 0.004 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".