Development of Image Overlay and Knowledge Transfer Module Technologies Aimed at Enhancing Feasibility and External Validation of Magnetic Resonance Imaging-based Scoring Systems
Bibliographic record
Abstract
OBJECTIVE: Semiquantitative arthritis scoring assesses disease burden by scoring presence/extent of features such as bone marrow lesion (BML) or effusion in multiple anatomic regions at a joint. An image overlay clarifying region borders may enhance feasibility and reliability of these scoring systems. To be scalable for use in large clinical trials, systematic computer-based user training is desirable. We developed an overlay and user training module for magnetic resonance imaging (MRI)-based scoring of hip osteoarthritis (OA). METHODS: We designed a semitransparent 2-dimensional image overlay applied to individual MRI slices to facilitate hip OA scoring [HIMRISS (Hip Inflammation MRI Scoring System)], initially using freeware and then in a customized HTML Web browser environment. We developed a systematic knowledge translation package including instructional presentation, fully scored expert consensus cases, and video tutorials for training in the use of these scoring systems with the overlays. Three musculoskeletal radiologists who had not used this scoring system before each performed a scoring exercise with no overlay, then repeated this with overlays after completing the training module. Based on postexercise interviews and a reader survey, we identified and corrected problems in the module. The entire training process was then repeated using 3 new readers. RESULTS: Overlays were considered useful, particularly when integrated into a Web browser. The knowledge translation module was considered conceptually valuable, but as initially implemented was too lengthy and not sufficiently interactive. CONCLUSION: Semitransparent image overlays and standardized knowledge translation modules for reader training show promise to facilitate reader calibration using MRI-based scoring systems. Based on our experience, knowledge translation modules should emphasize close feedback evaluating performance and reader time efficiency.
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 | 0.013 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".