P.032 Using clinical MRI scans for research purposes: a preliminary feasibility study
Bibliographic record
Abstract
Background: This project aims to bridge the gap between clinical data being collected at a local hospital to be used for research. Methods: 1.5T high-resolution anatomical MRI scans were collected from ten participants who were already undergoing clinical, imaging, and neurological assessment as part of their standard-of-care. Additional statistical models were used to examine the relationship between grey matter (using voxel-based morphometry [VBM]) and scores on the Toronto Cognitive Assessment (TorCA). Results: There was a lack of consistency in MRI scanning protocols and inconsistent reporting of clinical and neuropsychological data across participants. No significant relationship was found using the p-corrected images at p < 0.05. When viewing uncorrected images at a threshold of p < 0.001, we found a significant positive correlation between TorCA scores in the areas of the bilateral superior frontal gyrus, frontal pole, brain stem, and left putamen. Conclusions: Although no significant relationship was found between VBM metrics and TorCA scores, this project represents a crucial step in connecting health research with clinical practice where neuroimaging and neuropsychological assessments are already being collected. This project also informed our research team of areas that are needing to be streamlined and operationalized in future strategies for data collection and input.
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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 | 0.105 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".