Trust and Reciprocity: Foundational Principles for Human Subjects Imaging Research
Bibliographic record
Abstract
There is no greater asset to human subjects research than human subjects themselves.While curiosity, the lure of a monetary incentive or a keepsake brainscan, or the occasional hope for a medical explanation for an undisclosed complaint may underly a subject's decision to participate in research involving magnetic resonance imaging (MRI), for example, altruism is a fundamental driving force.Given the terms of the research contract between the investigator and the participant, any benefit in the form of new knowledge obtained accrues to the investigator in the short term and, if an experiment is successful and has translational potential, possibly to society in the long term.Other than possible psychological benefits and a sense of worthiness, 1 direct benefits to the participant are not expected.This is a feature of the investigator-subject relationship that must be conveyed by the investigator in verbal and written consent.Participant's altruism, coupled with professional responsibility and professional codes of ethics, therefore, make trust and reciprocity foundational principles in the scientific process.One important aspect of promulgating these principles is full disclosure of risks of the research, as discussed by Marshall et al for MRI in this volume.Magnetic resonance imaging, with its excellent signal to noise ratio and flexible tissue contrast brought clinical diagnosis to new heights in the 1980s.The MRI also quickly transformed research imaging with the ability to tap anatomy noninvasively and repeatedly and, in 1990s, brought functional imagingmethods which measure changes in blood oxygenation in response to discrete stimuli -to the foreground. 2 Research Ethics Boards (REBs) variously classify research with MRI as minimal to moderate risk, depending in part on the use of contrast agents, sedation, and the age and vulnerability of the population.There are known risks to human subjects that merit caution.For example, claustrophobia, metal implants, sensitivities to particular stimuli (spanning the range of olfactory to emotionally charged stimuli), and certain electronic devices such as stimulators and pacemakers are contraindications to participation in an MRI study.Other risks are more speculative and are "known unknowns."Some notable examples are the long-term effects of chronic exposure to magnetic fields as high at 9.4T, and the effects of MRI on Fetuses.Marshall et al bring together a discussion of many of these variables focused on structural imaging under the one roof of their paper.In the analysis of these risks, the authors raise important questions about current and future challenges of disclosure.At the present time, there is no empirical answer to the question of whether exposure to magnetic fields of any field strength for human experimental purposes constitutes any real Can.
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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 | 0.055 | 0.129 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.045 | 0.070 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".