Low-tube-voltage combined with adaptive statistical iterative reconstruction-V technique in CT venography of lower limb deep vein thrombosis
Bibliographic record
Abstract
Abstract This study contains 2 arms: (1) the ASIR-V technique combined with low-tube-voltage in lower limb deep vein thrombosis (DVT) diagnosis was investigated; and (2) CT venography and ultrasound results in DVT diagnosis were compared. For arm 1, 90 patients suspected of DVT were randomly divided into 3 groups (30/group): groups A and B were scanned under 100-kV with pre-set ASIR-V weights of 30% and 50% respectively; group C were scanned under 70-kV with a 50% weight. For arm 2, 75 patients were divided into 3 groups (25/group), each group was CT scanned as in arm 1 and then all subjects were examined by ultrasound. Groups A, B and C had 16, 14 and 17 patients diagnosed with DVTs, respectively. There was no significant difference in subjective ratings of image quality among all groups. The 70-kV protocol remarkably increased venous attenuation value while all groups had similar DVT attenuation value. Higher noise was observed in group C, the CNR however, was actually augmented due to elevated venous attenuations. More importantly, group C had significantly lower CTDIvol and DLP values. In conclusion, the 70-kV protocol is superior to the 100 kV protocols, which was supported by findings from the second arm study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".