Practical Application of Polysomnography in Infants
Bibliographic record
Abstract
This manuscript presents a comprehensive narrative review of the applications of polysomnography in infants. Considering the growing interest in the early identification of sleep-disordered breathing and its impact on the development of the nervous system, this is an exceptionally important and clinically relevant topic. There is a significant need for a proper understanding of the concept of polysomnography, which would enable its appropriate use in both diagnosis and treatment. This issue becomes particularly important given the limited number of scientific reports addressing the neonatal and infant periods. Objective: The usefulness of polysomnography during the first year of a child’s life. Methods: Between February and August 2025, a review of publications presenting aspects of polysomnography was conducted. Special attention was given to studies concerning the infant period, published between January 2015 and January 2025. The selection was carried out through the PubMed National Library of Medicine search engine, using the following keywords: “polysomnography”, “obstructive sleep apnea”, and “infant.” Results: Based on detailed inclusion criteria, 90 out of 1200 publications were qualified for analysis. Conclusions: Polysomnography is used both in the diagnostic process and in qualification for surgical treatment. It enables actions aimed at multidisciplinary management that improve patient outcomes while simultaneously reducing factors that worsen prognosis. At the same time, its usefulness in evaluating the therapeutic process and assessing improvement after both noninvasive and invasive interventions should be emphasized.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".