Current Practices of Antiseptic Use in Canadian Neonatal Intensive Care Units
Bibliographic record
Abstract
OBJECTIVE: This article assesses the degree of variability in the current practice of skin antiseptics used in Canadian neonatal intensive care units (NICUs) and different experiences related to each antiseptic used. METHODS: An anonymous survey was distributed to a clinical representative of each of the 124 Canadian level II and level III NICUs. RESULTS: One hundred and two respondents (82.2%), representing all Canadian provinces, completed the survey. Chlorhexidine gluconate with/without alcohol was the antiseptic most used (96%) and the antiseptic with the highest reported adverse effects (68% reported skin burns/breakdown). Other antiseptics used include povidone-iodine (35%) and isopropyl alcohol (22%). Specific guidelines for antiseptic use were available in only 50% of the units with many NICUs lacking gestational and/or chronological age restrictions. Only 23% of responders believed that there was awareness among health care providers of the adverse effects of antiseptics used. Less than half (43%) were completely satisfied with the antiseptics used in their units. CONCLUSION: Chlorhexidine gluconate is the most commonly used antiseptic in Canadian NICUs. The high number of associated adverse effects and the lack of guidelines regulating antiseptic use are of concern. Large clinical trials are urgently needed to guide practice and improve the safety of antiseptics.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".