Incidental Findings in Neuroimaging Research: A Framework for Anticipating the Next Frontier
Bibliographic record
Abstract
While strategies for handling unusual and possibly clinically significant anatomical findings on brain scans of research volunteers have been developed and implemented across neuroimaging laboratories worldwide, few concrete steps have been taken to consider the next frontier: functional anomalies. Drawing on the genetics literature, early work in neuroimaging considered actionability to be a driving force for determining if and when findings should be disclosed to individuals in whom they are detected, as inherent uncertainty raises potential ethical dilemmas of misdiagnosing and mislabelling people as patients. Here we consider the possibility of incidental findings in brain function during the resting state. Our approach does not anchor the resting state as the sine qua non of functional incidental findings, but as a path to thinking about where they may emerge in the future and how our professional communities need to think about thinking about them. We suggest that considering the issues proactively today, within a framework that is maximally flexible and open to modification, is better than responding reactively after the fact and with no framework at all. We argue that there is a duty to consider possible incidental findings despite the ambiguities of data interpretation, while working hard to prevent unnecessary alarm.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Research integrity Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
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 | 0.393 | 0.276 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.003 |
| Science and technology studies | 0.016 | 0.219 |
| Scholarly communication | 0.029 | 0.049 |
| Open science | 0.010 | 0.026 |
| Research integrity | 0.033 | 0.037 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".