Efficacy analysis of ‘Consumptive thirst bi disease treatment program verification program’ on 480 clinical cases
Bibliographic record
Abstract
Objective: State Administration of traditional Chinese medicine'eleventh five-year'key Specialist(disease) of diabetic peripheral neuropathy cooperation and group member units determined the method of clinical efficacy and safety,and laid the foundation for further optimization scheme through the diabetes syndrome treatment scheme for authentication.Methods: using a randomized,multicenter,before and after treatment with self control,according to the patients,order and the patients,wishes incorporated them into the treatment group,treatment group according to syndrome differentiation with corresponding prescription and external treatment.Two weeks were a course of treatment.Observation after a treatment observed in 480 cases before and after treatment,including clinical symptoms and signs,blood glucose,blood lipids,Toronto clinical score and safety indicators,and efficacy analysis and safety assessment.Results: the diabetes disease rheumatism authentication scheme can significantly improve patients numb,cold,pain,flaccidity symptoms,improve blood glucose,lipid,Toronto clinical score,the total efficiency of 95%.Conclusions: diabetes rheumatism verification scheme can effectively alleviate the hemp,cool,pain,impotence symptoms,improve blood glucose,blood lipids,Toronto clinical score lower,is a set of reliable curative effect,safety and convenient treatment,worthy of clinical application.
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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".