Protocol of Trials within Cohorts on the Integrated traditional Chinese and western medicine in the treatment of chronic spontaneous urticaria (Preprint)
Bibliographic record
Abstract
<sec> <title>BACKGROUND</title> Chronic spontaneous urticaria (CSU) is a common inflammatory skin disease with severe itching and wheals. About 1.4% population worldwide are affected. Neither Second-generation H1-receptor antagonists nor biological agent Omalizumab (OMA) have low efficacy rate. Previous studies indicate that Traditional Chinese Medicine combined with biological agents can improve efficacy and reduce the occurrence of side effects in inflammatory skin diseases. This study initiates a Trials within Cohorts (TwiCs) method study, aiming to confirm the efficacy and safety of the Shenqi Formula (SQF) combined with OMA for CSU treatment </sec> <sec> <title>OBJECTIVE</title> To confirm the efficacy and safety of the Shenqi Formula (SQF) combined with OMA for CSU treatment </sec> <sec> <title>METHODS</title> This study is a cohort-based trial method named TwiCs, in which the urticaria cohort was obtained starting in November 2019 at Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine. 92 patients in this cohort will be randomly allocated into two groups: the OMA combined with SQF group and the OMA group in a 1:1 ratio. Patients will receive a 24-week treatment with 16 weeks of follow-up. The primary outcome is the 7-day Urticaria Activity Score. </sec> <sec> <title>RESULTS</title> No results are available as data collection is ongoing. </sec> <sec> <title>CONCLUSIONS</title> No conclusions are available as data collection is ongoing. </sec> <sec> <title>CLINICALTRIAL</title> International Traditional Medicine Clinical Trail Registry, ITMCTR2025000308, Registered 28 January 2025, http://itmctr.ccebtcm.org.cn/zh-CN/UserPlatform </sec>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".