Efficacy and safety of qiming granule combined with laser in the treatment of diabetic retinopathy
Bibliographic record
Abstract
BACKGROUND: Diabetic retinopathy is not only the most common complication of diabetes, but also 1 of the main causes of blindness, which seriously affects the physical and mental health of patients. Panretinal photocoagulation is a common method for the treatment of diabetic retinopathy, but it has some defects. Qiming granule has advantages in the treatment of diabetic retinopathy, but there is a lack of standard clinical research to verify it. Therefore, the purpose of this randomized controlled trial is to evaluate the efficacy and safety of qiming granule combined with laser in the treatment of diabetic retinopathy. METHODS: This is a prospective randomized controlled trial to study the efficacy and safety of Qiming granule combined with laser in the treatment of diabetic retinopathy. Approved by the Clinical Research Society of our hospital. The patients are randomly divided into a treatment group (Qiming granule combined with laser treatment group) or control group (simple laser treatment group). The patients are followed up for 12 months after 6 months of treatment. Observation indexes include total effective rate, corrected visual acuity, macular fovea thickness, adverse reactions and so on. Data are analyzed using the statistical software package SPSS version 18.0 (Chicago, IL). DISCUSSION: This study will evaluate the clinical efficacy and safety of qiming granule combined with laser in the treatment of diabetic retinopathy. The experimental results of this study will provide a reliable reference basis for clinical use of qiming granule combined with laser in the treatment of diabetic retinopathy. TRIAL REGISTRATION: OSF Registration number: DOI 10.17605/OSF.IO/ZEQPB.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".