Bibliographic record
Abstract
Many patients, physicians, and sometimes even academics have questionable perceptions of placebo and the so-called placebo effect, Many believe that placebo have its own effects. Although psychological aspects, namely expectations of patients or the persuasive power of the physicians, might sometimes be substantial, such aspects may have little or even no relevance in other situation where placebo control is essential nevertheless. Even in settings where psychological effects should be envisaged, their extent is usually highly variable, indicating that other factors might still exceed the importance of psychological effects. Placebo is defined in US regulations as an inactive preparation designed to resemble the test drug as far as possible. This means that placebo itself cannot be effective. If it would, its correctness is challenged and it should be replaced if still possible. And as placebo is not effective, it can also not have secondary effects, vulgo side effects. Placebo is always used for two reasons: To control bias and to provide the reasonably largest delta, i.e. the difference between two treatments. Placebo should never be interpreted as being able to cause effects.
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.
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 | 0.061 | 0.185 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.052 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.015 | 0.041 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".