Bibliographic record
Abstract
Objective To investigate the optimized parameters of multi slice CT virtual colography (MSCT VC). Methods Eight segments of pig colon were resected and cleaned, and simulated polyps with different size were created on the mucosa of colon. Each specimen was scanned with collimation of 1.0 mm, 2.5 mm, 5.0 mm, and pitch of 1.25, 1.75, reconstructed with 0%, 50%, 70% overlap, respectively. The images were classified into 16 groups by different parameters, and VC with volume perspective mode were processed. The accuracy of polyps detection was evaluated in each group. Results Group 1 (collimation 1.0mm, Pitch 1.25, overlap rate 50%) had the maximal accuracy of polyps detection, but there was no significant difference between group 1 and group 2-4, 9-12. Group 12 (collimation 2.5mm, Pitch 1.75, overlap rate 50%) had the least scan time and CTDI, but VC with volume perspective mode was superior to that with surface perspective mode. Conclusion In experimental condition, MSCT VC were significantly affected by the collimation and overlapping rate. The optimized parameters were as follows: collimation 2.5 mm, pitch 1.75 and 50% overlapped reconstruction, and with volume perspective VC.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".