Reliability and sensitivity to change of IW-TSE versus DESS magnetic resonance imaging sequences in the assessment of bone marrow lesions in knee osteoarthritis patients: Longitudinal data from the Osteoarthritis Initiative (OAI) cohort
Bibliographic record
Abstract
Background: Bone marrow lesions (BMLs) are associated with osteoarthritis (OA). We assessed the performance of two commonly used MRI sequences, IW-TSE and DESS, for reliability in the detection of BMLs and sensitivity to estimate change over time. We suggested that the IW-TSE would demonstrate higher sensitivity to change than DESS in the assessment of BML prevalence and change over time. This study was performed using a subset of the Osteoarthritis Initiative (OAI) cohort. Methods: A sub-group of 144 patients was selected from the OAI progression cohort who all had IW-TSE and DESS MRI acquisitions at baseline and 24 months. BMLs were assessed using a semi-quantitative scale in the global knee, medial and lateral compartments, and subregions. Intra-reader reliability was assessed on a subset of 51 patients. Results: Intra-reader reliability was substantial for the global knee ≥ 0.64, medial ≥ 0.70, and lateral ≥ 0.63 compartments for IW-TSE and DESS. The prevalence of BML detected at baseline was only slightly greater for IW-TSE compared to DESS. The mean BML score at baseline was significantly higher (p ≤ 0.006) for the IW-TSE than the DESS. However, mean change at 24 months was similar for both sequences for all regions except the medial compartment (p = 0.034) and medial femur (p = 0.015) where they were significantly higher for DESS than IW-TSE. Moreover, the prevalence of BML change at 24 months was similar in all regions except the global knee (p = 0.047) and the lateral tibial plateau (p = 0.031). Conclusion: This study does not suggest superior sensitivity to change of one sequence over the other for almost all the regions. The only difference is a higher BML mean change over time detected by the DESS sequence in the medial compartment and femur. These data bring into perspective that both sequences seem equivalent regarding their use for the assessment of BML in clinical trials.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".