Assessing digital tomosynthesis for paediatric sacroiliac joint imaging
Bibliographic record
Abstract
Objective: To assess the use of digital tomosynthesis (DT) as an alternative imaging modality to radiography for the imaging of paediatric sacroiliac (SI) joints. Methods: Three anthropomorphic pelvis phantoms were imaged using DT at three dose levels: equivalent to the dose incurred in radiography (tube current-time product of 0.32 mAs), almost double the dose incurred in radiography (0.63 mAs) and the default dose setting of the DT system (1.6 mAs). Radiographs of the phantoms were also acquired for comparison. Six radiologists were asked to compare DT images to radiographs and rate DT as either better, the same or worse than radiography. Results: An exact Chi-square test was performed on the data and showed no significant difference in preference for DT between the three phantoms (exact p-value = 0.3389). There was a significant difference in preference for DT between radiologists as well as between the three dose settings (exact p-value = 0.0095 and 0.0001, respectively). At a mAs of 0.32 there is no clear preference for DT, however as mAs increases so does preference for DT. Conclusions: DT shows promise as a possible imaging alternative to radiography, although at dose levels higher than radiography. DT may prove advantageous in SI joints imaging if it reduces the need for subsequent computed tomography imaging which is typically used when radiography yields equivocal results.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".