Counting your chickens before they hatch: improvements in an untreated chronic pain population, beyond regression to the mean and the placebo effect
Bibliographic record
Abstract
Introduction: Isolating the effect of an intervention from the natural course and fluctuations of a condition is a challenge in any clinical trial, particularly in the field of pain. Regression to the mean (RTM) may explain some of these observed fluctuations. Objectives: In this paper, we describe and quantify the natural trajectory of questionnaire scores over time, based on initial scores. Methods: Twenty-seven untreated chronic low back pain patients and 25 healthy controls took part in this observational study, wherein they were asked to complete an array of questionnaires commonly used in pain studies during each of 3 visits (V1, V2, V3) at the 2-month interval. Scores at V1 were classified into 3 subgroups (extremely high, normal, and extremely low), based on z-scores. The average delta (∆ = V2 - V1) was calculated for each subgroup, for each questionnaire, to describe the evolution of scores over time based on initial scores. This analysis was repeated with the data for V2 and V3. Results: Our results show that high initial scores were widely followed by more average scores, while low initial scores tended to be followed by similar (low) scores. Conclusion: These trajectories cannot be attributable to RTM alone because of their asymmetry, nor to the placebo effect as they occurred in the absence of any intervention. However, they could be the result of an Effect of Care, wherein participants had meaningful improvements simply from taking part in a study. The improvement observed in patients with high initial scores should be carefully taken into account when interpreting results from clinical trials.
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How this classification was reachedexpand
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 | 0.042 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".