Electromechanical probe and automated indentation maps are sensitive techniques in assessing early degenerated human articular cartilage
Bibliographic record
Abstract
Recent advances in the development of new drugs to halt or even reverse the progression of Osteoarthritis at an early-stage requires new tools to detect early degeneration of articular cartilage. We investigated the ability of an electromechanical probe and an automated indentation technique to characterize entire human articular surfaces for rapid non-destructive discrimination between early degenerated and healthy articular cartilage. Human cadaveric asymptomatic articular surfaces (four pairs of distal femurs and four pairs of tibial plateaus) were used. They were assessed ex vivo: macroscopically, electromechanically, (maps of the electromechanical quantitative parameter, QP, reflecting streaming potentials), mechanically (maps of the instantaneous modulus, IM), and through cartilage thickness. Osteochondral cores were also harvested from healthy and degenerated regions for histological assessment, biochemical analyses, and unconfined compression tests. The macroscopic visual assessment delimited three distinct regions on each articular surface: Region I was macroscopically degenerated, region II was macroscopically normal but adjacent to regions I and III was the remaining normal articular surface. Thus, each extracted core was assigned to one of the three regions. A mixed effect model revealed that only the QP (p < 0.0001) and IM (p < 0.0001) were able to statistically discriminate the three regions. Effect size was higher for QP and IM than other assessments, indicating greater sensitivity to distinguish early degeneration of cartilage. When considering the mapping feature of the QP and IM techniques, it also revealed bilateral symmetry in a moderately similar distribution pattern between bilateral joints. © 2016 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:858-867, 2017.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".