Quality and credibility of clinical practice guidelines recommendations for the management of neonatal hypoglycemia. A protocol for a systematic review and recommendations’ synthesis
Bibliographic record
Abstract
INTRODUCTION: Hypoglycemia is one of the most frequent metabolic conditions in neonates. Clinical practice guidelines (CPGs) influence clinical practice as high-quality CPGs facilitate the use of evidence in practice. This proposed study aims to systematically identify and appraise CPGs and CPG recommendations (CPGRs) for treating neonatal hypoglycemia (NH). METHODS AND ANALYSIS: We will conduct searches in MEDLINE, EMBASE, CINAHL, Cochrane Library, LILACS (Latin American & Caribbean Health Sciences Literature), and Epistemonikos. Authors will search CPGs-specific databases and grey literature. Two reviewers will independently perform the titles and abstract screening, full-text review, and data extraction. Two appraisers will assess the quality of the CPGs and their recommendations using AGREE II (Appraisal of Guidelines Research and Evaluation) and AGREE-REX (Appraisal of Guidelines Research and Evaluation-Recommendations Excellence) instruments. Scores of ≥ 60% in the rigour of development domain will be considered for defining high-quality with AGREE II tool. CPGRs with scores >60% in the three domains will be used to determine high quality with the AGREE REX tool. We will perform a synthesis of the CPGRs to identify the consistency among the CPGRs and the methodological quality of primary studies that support them. ETHICS AND DISSEMINATION: The results will help us to identify the methodological and quality gaps in the existing CPGs for the treatment of NH. Our findings will be submitted to peer-review journals and presented at academic conferences. Based on the study design, approval from the institutional ethics board is not required for this project. TRIAL REGISTRATIONS: Systematic Review Registration Number (PROSPERO): CRD 42021239921.
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 | 0.246 | 0.431 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.020 | 0.029 |
| Bibliometrics | 0.028 | 0.022 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.039 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".