Acute Biochemical alterations during Phototherapy of Neonatal Icterus: Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Purpose Phototherapy is used in the treatment of neonatal hyperbilirubinemia. There are contravening reports on its its adverse effect. This meta-analysis was conducted to substantiate the impact of phototherapy on the parameters indicating oxidative stress and proinflammatory cytokines. Methods The relevant clinical studies were searched on PubMed, Google Scholar, Stanford University High-Wire Press, Clinical trial registry, and on Semantic Scholar published up to 30th July 2022. Newcastle-Ottawa Scale was used to assess the study qualities. The overall estimate was calculated in terms of odds ratio with 95% confidence interval using random effect model. The sensitivity and subgroup analysis were carried out along with the qualitative assessment of the publication bias. All the analysis was carried out using RevMan 5. Results Total 1735 participants from 31 pertinent studies were used for the quantitative analysis. The pooled estimates of the phototherapy-exposed group were compared with the control group. Our analysis revealed that phototherapy used in treating neonatal jaundice induced significant oxidative stress and increased the levels of inflammatory cytokines. Overall estimate measure i.e., mean difference was found to be significant [1.39 (0.79 1.99)] for MDA (nmol per ml), [-0.87 (-1.35 -0.39)] for MDA (nmol per litre) [-0.25 (-0.34 -0.15)] for TAC, [-81.68 (-105.50 -57.85)] for TNF-α, and non-significant for TOS and IL-6 i.e [1.36(-2.67 5.39)] and [1.96 (-0.89 4.81)] respectively Conclusion Therapeutic exposure to phototherapy in treating neonatal jaundice induces a rise in oxidative stress and inflammatory cytokine levels. The short and long-term clinical outcomes may have clinical significance.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.029 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".