Validation of assessment methods for the apparent diffusion coefficient in a clinical trial of axial spondyloarthritis patients treated with golimumab
Bibliographic record
Abstract
Purpose To compare three region-of-interest (ROI) settings in the assessment of ADC in a clinical trial, and to evaluate the effectiveness of ADC in assessing therapy-induced changes and predicting clinical outcomes. Methods In a 52-week clinical trial involving patients with axial spondyloarthritis, mean sacroiliac joint (SIJ) ADC measurements using structured, lesion-based, and index-lesion ROI-settings were assessed at baseline and weeks 4, 16, and 52. Variation among the three ROI-settings, correlations with Spondyloarthritis Research Consortium of Canada (SPARCC)-bone marrow edema (BME) SIJ inflammation indices, standardized response means (SRMs), and effectiveness in predicting clinical outcomes were analyzed. Results Forty of the 53 patients had at least one assessable SIJ lesion on ADC at baseline. The mean of the structured ROI ADC (ADC struc ) was 230 μmm 2 /s (standard deviation [SD] = 120). This was significantly lower ( p < 0.01) than the means of the lesion-based ROI ADC (ADC lesion = 420 μmm 2 /s, SD = 210) and index-lesion ROI ADC (ADC index = 471 μmm 2 /s, SD = 278), which did not differ. ADC correlated with SPARCC-BME scores at baseline ( p < 0.01) as did changes over time in ADC- and SPARCC-BME ( p <0.05). At all follow-up time points, responsiveness was high for ADC lesion (SRM > 0.92) and ADC index (SRM > 0.87) while moderate for ADC struc (SRM:0.54-0.67). Baseline ADC and changes in ADC did not predict clinical outcomes. Conclusions Lesion-based and index-lesion ROI ADC could both be used to evaluate the effectiveness of tumor necrosis factor inhibitor therapy. None of the methods could predict clinical outcomes.
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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 | 0.067 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".