Do all placebos fit the definition of a “placebo”? The variation in glycemic response of different placebos in healthy individuals
Bibliographic record
Abstract
BACKGROUND Placebo is defined as an inert substance not possessing any biological effect. But this definition may not apply to all placebos. We previously noticed that a cornstarch‐placebo decreased the glycemic response to a 75g‐oral glucose tolerance test (75g‐OGTT) compared with a water‐control. Our objective was to investigate the effect of five common placebos on glycemia. METHODS Using a double‐blind, randomized, multiple‐crossover design, 10 healthy subjects (gender:6M:4F, age:33.1±4y, BMI:27.1±1.7kg/m2) received 7 treatments: 9g glucose‐placebo, lactose‐placebo, lactulose‐placebo, wheat‐bran‐placebo, and cornstarch‐placebo and two water controls. Each treatment was given 40‐min before a 75g‐OGTT with blood drawn at −40, 0, 15, 30, 45, 60, 90, and 120‐min. RESULTS Two‐way ANOVA showed a significant effect of treatment(p<0.001) and time (p<0.001) on incremental glycemia, with no interaction. Glucose‐placebo significantly reduced AUC by 41±15.4% (p<0.001) and peak glycemia (p<0.005) compared with the mean of the two water‐controls. It also reduced AUC (p<0.001) and peak glycemia (p<0.005) significantly compared with the cornstarch, lactulose and wheat‐bran placebos. CONCLUSIONS Glucose may not fit the definition of a true placebo. The implication is that the use of glucose as a placebo may lead to underestimation of efficacy in glycemic testing protocols. Travel grant: Inovobiologic, Calgary, AB.
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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 | 0.072 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".