Establishing a Standard for Creating Angle-Corrected, Reformatted Brain CT Images.
Bibliographic record
Abstract
PURPOSE: To establish a standardized method of reformatting axial images for computed tomography (CT) brain examinations. METHODS: An anatomic line between the superior orbital rim and the base of the occipital bone (SOR-BS line) was chosen as the standardized reference line. In June 2022, CT technologists at a tertiary care center received an educational presentation and a 1-page reference handout on making standardized CT reformats. This was the quality-of-care intervention. Subsequently, 100 CT brain examinations performed on July 1 to 10, 2020 (preintervention) were analyzed and compared with 100 CT brain examinations performed on July 1 to 10, 2022 (postintervention). RESULTS: = .67). However, the number of CT brain studies with an angle difference of more than 20° decreased from 4 studies to 1 study. In addition, the number of CT brain studies without reformatted images decreased from 5 to 2 studies. DISCUSSION: The cause for the less-than-optimal adoption of the expected change in CT workflow might be complex and multifactorial. However, the institution in this study is a busy tertiary care center with a chronic shortage of CT technologists. The busy workflow might have contributed to lack of significance for the parameters assessed. CONCLUSION: There was a slight but not significant improvement between preintervention and postintervention data.
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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 | 0.039 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".