Tetrapolar Measurement of Electrical Conductivity and Thickness of Articular Cartilage
Bibliographic record
Abstract
A tetrapolar method to measure electrical conductivity of cartilage and bone, and to estimate the thickness of articular cartilage attached to bone, was developed. We determined the electrical conductivity of humeral head bovine articular cartilage and subchondral bone from a 1- to 2-year-old steer to be 1.14+/-0.11 S/m (mean+/-sd, n =11) and 0.306+/-0.034 S/m, (mean+/-sd, n =3), respectively. For a 4-year-old cow, articular cartilage and subchondral bone electrical conductivity were 0.88+/-0.08 S/m (mean+/-sd, n =9) and 0.179+/-0.046 S/m (mean+/-sd, n =3), respectively. Measurements on slices of cartilage taken from different distances from the articular surface of the steer did not reveal significant depth-dependence of electrical conductivity. We were able to estimate the thickness of articular cartilage with reasonable precision (<20% error) by injecting current from multiple electrode pairs with different inter-electrode distances. Requirements for the precision of this method to measure cartilage thickness include the presence of a distinct layer of calcified cartilage or bone with a much lower electrical conductivity than that of uncalcified articular cartilage, and the use of inter-electrode distances of the current injecting electrodes that are on the order of the cartilage thickness. These or similar methods present an attractive approach to the non-destructive determination of cartilage thickness, a parameter that is required in order to estimate functional properties of cartilage attached to bone, and evaluate the need for therapeutic interventions in arthritis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".