Histological scoring of articular cartilage alone provides an incomplete picture of osteoarthritic disease progression.
Bibliographic record
Abstract
PURPOSE: To ascertain whether molecular subcategories of disease progression exist within established histological grades of articular cartilage (AC). METHODS: Based on H&E and safranin-O staining of AC sections obtained from 18 knee arthroplasty surgeries, 30 samples ranging from Mankin Scoring System grade 1 through 5 were identified. Immunohistochemical (IHC) analysis for collagen type II and aggrecan was performed on serial sections of the paraffin-embedded AC samples. Six AC samples from each of the five Mankin Scoring System grades were examined. RESULTS: Significant IHC differences in collagen type II and aggrecan deposition were seen within AC samples from all five histological grades. The range of IHC differences in collagen type II and aggrecan increased with increasing histological grade. A change in the pattern of collagen type II deposition was observed in MG-3 AC that was consistent with a switch in collagen type II metabolism. CONCLUSIONS: IHC staining of collagen type II and aggrecan can identify differences within histological grades of AC that are consistent with the existence of molecular subcategories. These differences were detectable even within the lowest histological grades; therefore the use of IHC staining can further enhance and refine the scoring of AC deterioration in early osteoarthritis (OA). Furthermore, the changes seen in the deposition pattern for both aggrecan and collagen type II suggest that they could be used to monitor key molecular events in OA progression. These findings also underscore the need for the development of IHC scoring criteria.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".