Topical nitroglycerin in newborns with ischemic injuries: A systematic review
Bibliographic record
Abstract
BACKGROUND: Arterial catheterization is frequently performed in neonatal intensive care units with an inherent risk of peripheral ischemic injury, especially in preterm infants. The treatment options following vascular damage involve invasive and non-invasive modalities. The primary objective of this systematic review was to evaluate the evidence of the use of topical nitroglycerine (TNG) either alone or as adjunctive therapy. The secondary aim was to develop an approach to the treatment of catheter induced ischemia in infants based on the available evidence. METHODS: A comprehensive search was conducted of available databases for relevant articles that involved the treatment of peripheral tissue ischemia in neonates with the use of TNG. Citations were restricted to human subjects. RESULTS: Six hundred and eighty-nine articles were identified, and twenty-seven case reports and case series were compatible with the inclusion and exclusion criteria. Sixty-eight infants out of the 76 published cases (89%) experienced a favorable outcome and 79% (n = 60) demonstrated complete recovery with the topical application of TNG to the ischemic site. CONCLUSION: The available evidence demonstrates that TNG is effective for the treatment of peripheral ischemia in neonates after standard conservative measures have failed. However, due to the absence of robust evidence for this therapeutic modality, there are no uniform guidelines regarding the frequency, duration, and safety of TNG use. Planning the management of peripheral ischemia in neonates with TNG should be a multidisciplinary decision that includes close surveillance of blood pressure, methemoglobin levels, and follow up cranial ultrasound.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".