Evidence-based Guidelines on Medication Therapy for Children with Vitamin D Deifciency:A Systematic Review
Bibliographic record
Abstract
Objective To systematically review the quality of evidence-based guidelines(EBGs) on medication therapy for children with vitamin D deficiency,and to compare differences and similarities of the drugs recommended,in order to provide guidance for clinical practice. Methods Databases such as the TRIP,Pub Med,EMbase,CNKI,VIP,Wan Fang Data,CBM,National Guideline Clearinghouse and Guidelines International Network were searched to collect EBGs on medication therapy for children with vitamin D deficiency. The methodological quality of the guideline was evaluated according to the AGREE II instrument,and the differences between recommendations were compared. Results A total of 9 EBGs were included. Among them,3 guidelines were developed by America,1 by Europe,1 by France,1 by China,1 by Poland,1 by Canadian and 1 guideline was by Australia and New Zealand. Seven guidelines were developed specially for children,while others were for people of different ages. According to the AGREE II instrument,only Scope and purpose and clarity and presentation were scored more than 60%. The recommendations of different guidelines were of large different. Conclusions The quality of included guidelines concerning children with vitamin D deficiency is vary. Although only the America 2011 guideline is of high quality,the strength of recommendation is not high. Thus,the development of national guidelines is urgently needed.
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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 | 0.021 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.022 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".