A Novel Algorithm Using Within-leg Calibration for Enhanced Accuracy of Detection of Arthritis by Infrared Thermal Imaging in Children
Bibliographic record
Abstract
OBJECTIVE: To standardize and improve the accuracy of detection of arthritis by thermal imaging. METHODS: Children with clinically active arthritis in the knee or ankle, as well as healthy controls, were enrolled to the development cohort; another group of children with knee symptoms was enrolled to the validation cohort. Ultrasound was performed in the arthritis subgroup for the development cohort. Joint exam by certified rheumatologists was used as a reference for the validation cohort. Infrared thermal data were analyzed using custom software. Temperature after within-limb calibration (TAWiC) was defined as the temperature differences between joint and ipsilateral mid-tibia. TAWiC of knees and ankles was evaluated using ANOVA across subgroups. Optimal thresholds were determined by receiver-operating characteristic analysis using Youden index. RESULTS: than that when using absolute temperature. Within the validation cohort, the sensitivity of accurate detection of arthritis in the knees using both mean and 95th TAWiC from individual views or all 3 views combined ranged from 0.60 to 0.70, and the specificity was > 0.90 in all views. CONCLUSION: Children with active arthritis or tenosynovitis in knees or ankles exhibited higher TAWiC than healthy joints. Our validation cohort study showed promise for the clinical utility of infrared thermal imaging for arthritis detection.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".