Therapeutic optimism in the consent forms of phase 1 gene transfer trials: an empirical analysis
Bibliographic record
Abstract
BACKGROUND: "Therapeutic misconception" arises when human subjects interpret a clinical trial as aimed primarily at therapy rather than producing knowledge. Therapeutic misconceptions may be more prevalent in trials enrolling gravely ill subjects or involving novel and well publicized investigational agents. OBJECTIVE: To examine the extent to which investigators express therapeutic optimism in phase 1 human gene transfer consent documents, whether highly active gene transfer researchers are more prone to expressing therapeutic optimism, and whether consent forms have grown more optimistic in their descriptions of personal benefit over the last decade. DESIGN: Content analysis was performed on 277 consent documents to measure the number of sentences describing possibility of benefit, terminology used for experimental agents, the proportion of statements describing personal versus societal benefits, and whether investigators attempted to thwart therapeutic misconceptions. RESULTS: Consent forms generally used therapeutic terminology to describe study agents, devoted more sentences to describing possible personal benefits than to describing benefits to society, and infrequently explained that a particular benefit was unlikely. Consent documents used by highly active gene transfer researchers tended to portray significantly greater optimism about personal benefit than less active investigators, though they were also significantly more cautious with agent terminology. Finally, therapeutic optimism expressed in consent forms has declined over the past decade. CONCLUSIONS: Consent documents used in phase 1 gene transfer trials, although increasingly attentive to possible therapeutic misconceptions, are inappropriately optimistic about direct benefits of trial participation. Such optimism is expressed more emphatically in trials involving highly active gene transfer researchers as principal investigators.
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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 | 0.408 | 0.808 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".