Ethical Problems of Observational Studies and Big Data Compared to Randomized Trials
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.088 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.015 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it