Investigation into the Individualized Treatment of Traditional Chinese Medicine through a Series of N‐of‐1 Trials
Bibliographic record
Abstract
Purpose. To compare the efficacy of individualized herbal decoction with standard decoction for patients with stable bronchiectasis through N‐of‐1 trials. Methods. We conducted a single center N‐of‐1 trials in 17 patients with stable bronchiectasis. Each N‐of‐1 trial contains three cycles. Each cycle is divided into two 4‐week intervention including individualized decoction and fixed decoction (control). The primary outcome was patient self‐reported symptoms scores on a 1–7 point Likert scale. Secondary outcomes were 24‐hour sputum volume and CAT scores. Results. Among 14 completed trials, five showed that the individualized decoction was statistically better than the control decoction on symptom scores (P < 0.05) but was not clinically significant. The group data of all the trials showed that individualized decoction was superior to control decoction on symptom scores (2.13 ± 0.58 versus 2.30 ± 0.65, P = 0.002, mean difference and 95% CI: 0.18 (0.10, 0.25)), 24 h sputum volume (P = 0.009), and CAT scores (9.69 ± 4.89 versus 11.64 ± 5.59, P = 0.013, mean difference and 95% CI: 1.95 (1.04, 2.86)) but not clinically significant. Conclusion. Optimizing the combined analysis of individual and group data and the improvement of statistical models may make contribution in establishing a method of evaluating clinical efficacy in line with the characteristics of traditional Chinese medicine individual diagnosis and treatment.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".