Comment on: Bone marrow lesions in people with knee osteoarthritis predict progression of disease and joint replacement: a longitudinal study: reply
Bibliographic record
Abstract
Sir, Thank you for the opportunity to respond to the letter from Crema and colleagues [1] about our recent article [2]. They have commented on the assessment of bone marrow lesions (BMLs) using MRI from a radiological perspective. While this is important, as previously mentioned in a reply [3] to a similar comment from the same group [4], these comments are made in the absence of any data comparing the reliability and sensitivity of T1- and T2-weighted images in detecting the presence of BMLs and their changes in size over time in knee OA patients. Indeed, a review of published studies does not seem to support this view, including publications from this group. The fact that T2-weighted sequences may show bigger BML size is not necessarily better and may be a simplistic view of this particular study and other similar works. Until accurate comparative studies are conducted, the above issue will remain based only on qualitative assessment and impression rather than conclusions from reliable scientific observations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".