Incidental findings in clinical research: the case of the ‘known unknowns’
Bibliographic record
Abstract
The practical implications of developing a standardized set of obligations for the research community to adhere to, in the management of current and future incidental findings (IFs), is a staggering exercise to contemplate. Consider the growing use of research biobanks (which is a repository of biological samples), the evolving field of genomic research, and the potential of patients seeking subsequent care based on a incidental clinical research finding could lead to significant additional time and resources to an already burdened enterprise [1]. Although maintaining the public’s trust in research remains vital; the stakes involved in how IFs are to managed are quite high for participants, researchers and the health system. In academia, seeking out common language and terminology are crucial to fostering productive dialogue in the exploration of any issue. However, the literature on IF continues to make reference to an unwieldy array of terms: abnormal, incidental, accidental, secondary, significant, unexpected, unrelated, unforeseen, unusual and variant, are some of the potential adjectives that have been used to precede the word ‘finding’ [2]. For the purposes of this editorial, I will apply a classic definition to explicate the concept of IF as it relates to clinical research: IF is a finding concerning an individual research participant that has potential health or reproductive importance and is discovered in the course of conducting research but is beyond the aims of the study [3]. For example, envision a medical imaging researcher that is examining structural attributes of the frontal cortex in healthy volunteers and in the course of research discovers that a participant has a glioblastoma, which is a brain tumor. In its earliest consideration, IFs were considered to be so rare and uncommon that researchers merely considered their discovery as being something that was stumbled upon in the moment and they had no idea whether or not to share this information with research participants. This phenomenon became affectionately called the ‘stumble strategy’ [4] and remained the status quo until the past decade. However, the following small sampling of recently reported occurrences of IF in clinical care tell another story:
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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 | 0.196 | 0.427 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.013 | 0.114 |
| Scholarly communication | 0.025 | 0.048 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.053 | 0.049 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".