Comparison of Average Blood Gas Parameters Between Arterial Blood & Capillary Blood in Neonates: A Protocol for Systematic Review and Meta-analysis
Bibliographic record
Abstract
Blood gas sampling and acid-base determination are essential tools in evaluating, managing, and monitoring neonates with respiratory or circulatory issues, especially those receiving respiratory support. Arterial Blood Gas & Capillary Blood Gas are two types of blood gas tests done in the neonatal ICU. Arterial punctures are painful and may cause arterial injury, thrombosis with distal ischemia, haemorrhage and aneurysm formation. Capillary blood gas samples are easier to obtain and are a less invasive way of evaluating blood gas parameters in neonates. This study protocol outlines the methodology for collating evidence from multiple studies that compare capillary blood gas and arterial blood gas and will help to find out whether capillary blood gas could be used as a reliable alternative to arterial blood gas. The review will retrieve information from three major databases: PubMed, Scopus & Web of Science. Literature screening and data extraction will be completed by two authors independently. Authors will use Newcastle-Ottawa Scale & Joanna Brigg’s Institute critical appraisal checklist to assess the included studies. Results will be visually represented through a forest plot, incorporating individual effect estimates, pooled estimates, and 95% confidence intervals. The EZR or STATA Software will be used to perform the meta-analysis. This systematic review will summarise the available evidence and contribute to our understanding in the controversy arising from conflicting studies on the reliability of capillary blood samples as a substitute to arterial samples. Key words: Blood gases; Meta-analysis; Neonates; Systematic review, pH, PCO2, PO2
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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 | 0.061 | 0.120 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.019 | 0.027 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.066 | 0.007 |
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".