Development of a Screening Tool for the Identification of Sacroiliitis in Computed Tomography Scans of the Abdomen
Bibliographic record
Abstract
OBJECTIVE: To develop a screening tool for the identification of sacroiliitis on abdominal computed tomography (CT) scan. METHODS: Variables including erosions (number and size), sclerosis (depths of > 0.3 cm or > 0.5 cm), and ankylosis were identified through a training exercise involving 12 CT scans containing the sacroiliac joints. Two blinded readers read 24 CT scans from a derivation cohort to propose a screening tool for identifying discriminating features of sacroiliitis. A test cohort of 68 patients was used to confirm the utility of this tool. Inter- and intraobserver values, sensitivity, specificity, and positive/negative likelihood ratios were calculated for individual as well as combinations of variables. Erosions were evaluated using receiver-operating characteristic curves. RESULTS: Analysis of the derivation cohort determined that counting the number of erosions on the worst coronal slice in each of 4 articular surfaces was not inferior to analyzing each individual slice in either transverse or coronal view. In the test cohort, interreader reliability for ankylosis and iliac and sacral erosions was very good (κ = 1, ICC = 0.989 and 0.995, respectively) whereas for sclerosis, it was moderate (κ = 0.39-0.96). A total erosion score of ≥ 3 was found to have the highest sensitivity and specificity for sacroiliitis (91% for each). The addition of a > 0.5 cm of iliac sclerosis or a > 0.3 cm of sacral sclerosis marginally increased the sensitivity (94%) but decreased specificity (85%). CONCLUSION: The presence of ankylosis or a total erosion score of ≥ 3 on CT is sufficient for identifying patients at high risk of sacroiliitis and may prompt more timely referrals to a rheumatologist.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".