Pediatric antifungal therapy. Part II: neonatal infections.
Bibliographic record
Abstract
Preterm Infants' survival has greatly increased in the last few decades thanks to the improvement in obstetrical and neonatal care. These neonates constitute the large majority of the population in neonatal intensive care units. The correct evaluation of postnatal growth of these babies is nowadays of primary concern, although the definition of their optimal postnatal growth pattern is still controversial. Concerns have also been raised about the strategies to monitor their growth,specifically in relation to the charts used. At present the available charts in clinical practice are fetal growth charts, neonatal anthropometric charts and postnatal growth charts for term infants. None of these, for different reasons, is suitable to correctly evaluate preterm infant growth. An international multicentric project has recently started a study aiming at building a prescriptive standard for the evaluation of postnatal growth of preterm infants and it will be available in the next years. At present, while an international longitudinal standard for evaluating preterm infant postnatal growth is lacking, in Italy the best compromise in clinical practice is likely to be as follows: new Italian INeS (Italian Neonatal Study) charts up to term; International longitudinal charts WHO 2006 or CDC 2002 from term to two years; finally, the Italian Society for Pediatric Endocrinology and Diabetes (SIEDP) 2006 growth charts could be suitable for monitoring the growth of these infants from two years up to 20 years of age.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".