Ethical Problems of Observational Studies and Big Data Compared to Randomized Trials
Bibliographic record
Abstract
The temptation to use prospective observational studies (POS) instead of conducting difficult trials (RCTs) has always existed, but with the advent of powerful computers and large databases, it can become almost irresistible. We examine the potential consequences, were this to occur, by comparing two hypothetical studies of a new treatment: one RCT, and one POS. The POS inevitably submits more patients to inferior research methodology. In RCTs, patients are clearly informed of the research context, and 1:1 randomized allocation between experimental and validated treatment balances risks for each patient. In POS, for each patient, the risks of receiving inferior treatment are impossible to estimate. The research context and the uncertainty are down-played, and patients and clinicians are at risk of becoming passive research subjects in studies performed from an outsider's view, which potentially has extraneous objectives, and is conducted without their explicit, autonomous, and voluntary involvement and consent.
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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 | 0.789 | 0.877 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.005 | 0.062 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.015 | 0.026 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".