Validation of an MRI‐based method to assess patellofemoral joint contact areas in loaded knee flexion in vivo
Bibliographic record
Abstract
PURPOSE: To develop and validate short axial and sagittal MRI scans (<1min) to assess in vivo patellofemoral contact areas in loaded knee flexion. MATERIALS AND METHODS: Contact area was assessed in four cadaver knee specimens from axial and sagittal scans using two contact area extraction techniques (delineation and intersection) and three calculation techniques (slice thickness multiplication, linear interpolation, and spline interpolation). Error was expressed as the mean absolute and percentage difference from a dye staining-based reference standard. Intrareader and intrasubject repeatability, expressed as the mean standard deviation, was determined. RESULTS: Contact area assessments from the sagittal MRI scans using the delineation and slice thickness multiplication technique had the smallest error (47.7 ± 38.1 mm(2) or 10.7%). The intrareader repeatability from assessments using the sagittal scans was smaller than those using the axial scans when the delineation method was used (<9.4 ± 4.3 mm(2) and <15.4 ± 14.1 mm(2) , respectively). The intrasubject repeatability of the assessment from the sagittal scan was less than 39.9 ± 23.0 mm(2) . CONCLUSION: This protocol yields assessments of contact area in less than 1 minute that have errors similar to those made using scans many times longer and can be used in series with kinematic scans to carry out simultaneous assessments in vivo to study patellofemoral joint disease.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.001 |
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".