Bibliographic record
Abstract
One of the unwritten parts of the job description of a trainee neurologist working in an academic institution can be the requirement to enrol into a brain MRI study as a research volunteer (‘normal control’ sounds too flattering for a neurologist). During my training, thrilled by the respite from requesting esoteric blood tests, I gladly lay on the research MRI scanner table and imagined my protons spinning under the influence of the magnet around me. But had I known that there was a 1 in 37 chance of identifying an incidental finding on brain MRI,1 the magnet would have seemed more like a European roulette wheel in which the ball may land in one of 37 pockets (gamblers, scholars and pedants who read Practical Neurology will know that this comparison breaks down in American roulette where the existence of a 38th double zero pocket offers the player longer odds). However, before deciding whether to be concerned about the chance, and perhaps the risk, of detecting incidental findings in the brain, it is important to clarify what they may be. In a meta-analysis of their prevalence in the brains of neurologically asymptomatic people, my colleagues and I eventually defined them as ‘apparently asymptomatic intracranial abnormalities that are clinically significant because of their potential to cause symptoms or influence treatment’ (this …
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.013 | 0.023 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".