Evaluating the Individualized Treatment of Traditional Chinese Medicine: A Pilot Study of N‐of‐1 Trials
Bibliographic record
Abstract
Purpose. To compare the efficacy of individualized herbal decoction with controlled decoction for individual patients with stable bronchiectasis. Methods. We conducted N-of-1 RCTs (single-patient, double-blind, randomized, multiple crossover design) in 3 patients with stable bronchiectasis. The primary outcome was patient self-rated symptom scores on visual analogue scales. Secondary outcome was 24-hour sputum volume. A clinical efficacy criterion which combined symptoms score and medication preference was also formulated. Results. All three patients showed various degrees of improvement on their symptoms and one patient's (Case 3) 24 h sputum volume decreased from 70 mL to 30 mL. However, no significant differences were found between individualized herbal decoction and control decoction on symptoms score, or on 24-hour sputum volume. One patient (Case 2) had clear preference for the individualized herbal decoction over the standard one with the confirmation after unblinding. We therefore considered this case as clinically important. Discussion. N-of-1 trials comply with individualized philosophy of TCM clinical practice and had good compliance. It is necessary to set up clinical efficacy criteria and to consider the interference of acute exacerbation.
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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 | 0.022 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".