Application of Magnetic Resonance T1rho and T2 Mapping in Evaluating Cartilage Injury in Middle-aged and Elderly Patients with Knee Osteoarthritis.
Bibliographic record
Abstract
Objective: To investigate magnetic resonance longitudinal relaxation time quantitative imaging (T1rho) and transverse relaxation time quantitative imaging (T2 mapping) techniques in evaluating cartilage damage in middle-aged and elderly patients with knee osteoarthritis (OA). Methods: To carry out this investigation, the researchers enrolled 65 OA patients subjects for the study. These patients were divided into 2 groups based on the severity of their OA. Thirty healthy individuals were included as the control group. All study participants underwent magnetic resonance T1rho and T2 mapping scans. OA patient scores and values from the Western Ontario and McMaster University Osteoarthritis Index (WOMAC), T2, and a T1rho MRI measurement indicating potential early indication of bone and joint diseases from each cartilage area were compared among the OA patients as well as the control group. Pearson correlation analysis was used to examine the relationships between T2 and T1rho values and WOMAC scores. Results: The WOMAC scores in the mild OA group were lower than the severe OA group (P < .05). There were no significant differences in T2 and T1rho values of lateral tibial cartilage among the 3 groups (P > .05). On the other hand, the T2 and T1rho values of medial femoral, lateral femoral, and medial tibial cartilage areas increased progressively in the control, mild OA, and severe OA groups (P < .05). A Pearson analysis found a positive correlation between the T2 values of medial, lateral, and medial tibial cartilages and the WOMAC scores. Similarly, the T1rho values of these cartilage areas were also positively correlated with the WOMAC scores. Conclusion: Magnetic resonance T1rho and T2 mapping offer good evaluation value for assessing cartilage injury in middle-aged and elderly patients with knee OA. The values obtained from T1rho and T2 mapping in various areas of the cartilage show a positive correlation with WOMAC scores.
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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 | 0.001 | 0.001 |
| 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 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".