Repeatability of focal changes in knee cartilage T2 relaxation times with load and time
Bibliographic record
Abstract
The purpose of this study is to assess the repeatability of focal changes in T 2 relaxation time of knee articular cartilage with load and over time. A short-term repeatability study was conducted in five healthy participants. Images were acquired of the knee in an unloaded position and in loaded flexion with a custom, quantitative Double Echo Steady State (qDESS) MRI sequence on a 3T scanner. The protocol was carried out three times within a one-week period. T 2 relaxation maps of the tibial and femoral articular cartilage were created and focal areas of change were identified using a cluster-based analysis approach; the outcome measure was defined as the percentage area covered by a cluster of pixels that change with load or over time. Average T 2 relaxation time within anatomical regions was also calculated. Repeatability of the cluster-based and regional approach was described as the root mean square standard deviation (SDrms) of the trials. The SDrms of the percentage area of the entire cartilage surface covered by clusters when the knees were loaded was less than 9.5%. The repeatability of the percentage area of the entire cartilage surface covered by clusters over time was less than 4.5% for the unloaded knees and 7.4% for the loaded knees. For the regional analysis, the average SDrms of the entire plate was less than 3.0 ms. Cluster analysis provides important information on focal changes in cartilage with the application of load and over time, but at the cost of lost repeatability compared to regional approaches.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".