État des lieux de la prescription d'ibuprofène aux nouveau-nés prématurés d'âge gestationnel inférieur à 32 semaines d'aménorrhée pour le traitement du canal artériel : données du CHU Amiens-Picardie de 2018 à 2019
Bibliographic record
Abstract
Objectives: Patent ductus arteriosus (PDA) is a common problem in neonatology. Despite three decades of research, its management remains an enduring controversy. Conservative, pharmacological, or surgical treatment can be used. Ibuprofen is used as first-line treatment in our center but it can lead to adverse effects. Our main objective was to estimate exposure to ibuprofen for the treatment of PDA in our center. Secondary outcome was to compare our practice to that of other equivalent centers. Method: We studied a retrospective cohort of 292 preterm infants born <32 WG, admitted to the NICU of CHU Amiens-Picardie between January 1st 2018 and December 31st 2019. Perinatal and neonatal characteristics were reviewed from electronic medical records. Exposure to ibuprofen and incidence of PDA were analyzed with respect to gestational age and body weight at birth. Data were compared to those of the Canadian Neonatal Network (CNN) 2018 report. Results : 271 preterm infants were included in our study. Rate of exposure to ibuprofen for PDA was analyzed according to gestational age at birth (WG) as follows: 71.3% in ≤ 26+6, 27.9% in 27+0 to 28+6, 17.7% in 29+0 to 30+6, and 2.6% in 31+0 to 31+6. According to birth weight (g) the rates of exposure were: 62.7% in <750, 44.6% in 750 to 999, 28.3% in 1000 to 1249, and 13.8% in ≥ 1250. In comparison to the CNN 2018 report, exposure to a medical treatment was significantly higher in our population, and incidence of PDA diagnosis was not statistically different. Conclusion : Exposure to ibuprofen was all the higher as gestational age and body weight were low at birth. Side effects and absence of benefits on major outcomes following a pharmacological treatment, but also high closure rate in centers using conservative management, lead us to question our elevated rate of exposure.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".