Effect of traditional Chinese medicine Bailing capsule on renal anemia in maintenance hemodialysis patients: A protocol for systematic review and meta-analysis.
Bibliographic record
Abstract
BACKGROUND: Renal anemia (RA) is one of the most common complications in patients with end-stage renal disease, and it is also one of the reasons for the decline of quality of life and functional status in patients with end-stage renal disease. Traditional treatment methods often fail to achieve satisfactory therapeutic effects, so it is very necessary to find effective adjuvant treatment methods. Bailing capsule (BLC), a traditional Chinese medicine, which has been widely used in the treatment of RA in maintenance hemodialysis patients, but a systematic review of the efficacy and safety of this drug is currently lacking. Therefore, this study used meta-analysis to evaluate the efficacy and safety of BLC in the treatment of RA, in order to provide guidance for finding effective auxiliary methods for the treatment of RA in maintenance hemodialysis patients (MHP). METHODS: Using the computer to retrieve PubMed, EMbase, Cochrane Library, CNKI, VIP Database, WANFANG Database, SinoMed from 1990 to 2021 and collecting the clinical randomized controlled trial and retrospective cohort study of BLC in the treatment of RA in MHP. Two researchers independently read and screened the literature, followed by evaluating the retrospective cohort studies that met the selection criteria using the Newcastle-Ottawa Scale (NOS) scale. The randomized controlled trial used the Cochrane manual standards to assess the risk of bias, and the RevMan 5.3 software was used to conduct a meta-analysis of the result data. RESULTS: This study will use the method of meta-analysis to evaluate the clinical efficacy and incidence of adverse reactions of BLC in the treatment of RA in MHP through the primary and secondary outcome indicators. CONCLUSION: The results of this study will help clinicians find safe and effective adjuvant therapy in the treatment of RA in MHP. OSF REGISTRATION NUMBER: DOI 10.17605/OSF.IO/732KP (https://osf.io/732kp).
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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 | 0.036 | 0.048 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.020 | 0.029 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.002 |
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".