Side effects can enhance treatment response through expectancy effects: an experimental analgesic randomized controlled trial
Bibliographic record
Abstract
In randomized controlled trials, medication side effects may lead to beliefs that one is receiving the active intervention and enhance active treatment responses, thereby increasing drug-placebo differences. We tested these hypotheses with an experimental double-blind randomized controlled trial of a nonsteroidal anti-inflammatory drug with and without the addition of atropine to induce side effects. One hundred healthy volunteers were told they would be randomized to either combined analgesics that might produce dry mouth or inert placebos. In reality, they were randomized double blind, double-dummy to 1 of the 4 conditions: (1) 100 mg diclofenac + 1.2 mg atropine, (2) placebo + 1.2 mg atropine, (3) 100 mg diclofenac + placebo, or (4) placebo + placebo, and tested with heat-induced pain. Groups did not differ significantly in demographics, temperature producing moderate pain, state anxiety, or depression. Analgesia was observed in all groups; there was a significant interaction between diclofenac and atropine, without main effects. Diclofenac alone was not better than double-placebo. The addition of atropine increased pain relief more than 3-fold among participants given diclofenac (d = 0.77), but did not enhance the response to placebo (d = 0.09). A chain of mediation analysis demonstrated that the addition of atropine increased dry mouth symptoms, which increased beliefs that one had received the active medication, which, in turn, increased analgesia. In addition to this indirect effect of atropine on analgesia (via dry mouth and beliefs), analyses suggest that among those who received diclofenac, atropine directly increased analgesia. This possible synergistic effect between diclofenac and atropine might warrant future research.
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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 | 0.075 | 0.086 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 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".