Systematic review of computed tomography parameters used for the assessment of subchondral bone in osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: To systematically review the published parameters for the assessment of subchondral bone in human osteoarthritis (OA) using computed tomography (CT) and gain an overview of current practices and standards. DESIGN: A literature search of Medline, Embase and Cochrane Library databases was performed with search strategies tailored to each database (search from 2010 to January 2023). The search results were screened independently by two reviewers against pre-determined inclusion and exclusion criteria. Studies were deemed eligible if conducted in vivo/ex vivo in human adults (>18 years) using any type of CT to assess subchondral bone in OA. Extracted data from eligible studies were compiled in a qualitative summary and formal narrative synthesis. RESULTS: This analysis included 202 studies. Four groups of CT modalities were identified to have been used for subchondral bone assessment in OA across nine anatomical locations. Subchondral bone parameters measuring similar features of OA were combined in six categories: (i) microstructure, (ii) bone adaptation, (iii) gross morphology (iv) mineralisation, (v) joint space, and (vi) mechanical properties. CONCLUSIONS: Clinically meaningful parameter categories were identified as well as categories with the potential to become relevant in the clinical field. Furthermore, we stress the importance of quantification of parameters to improve their sensitivity and reliability for the evaluation of OA disease progression and the need for standardised measurement methods to improve their clinical value.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".