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Record W2811049423 · doi:10.1055/s-0038-1661406

Current Practices of Antiseptic Use in Canadian Neonatal Intensive Care Units

2018· article· en· W2811049423 on OpenAlexafffundabout
Helen McCord, Elise Fieldhouse, Walid El‐Naggar

Bibliographic record

VenueAmerican Journal of Perinatology · 2018
Typearticle
Languageen
FieldHealth Professions
TopicNeonatal skin health care
Canadian institutionsIzaak Walton Killam Health CentreDalhousie University
FundersDalhousie University
KeywordsAntisepticMedicineChlorhexidineAdverse effectIntensive careChlorhexidine gluconateIntensive care medicineDentistryInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.594
Threshold uncertainty score0.778

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.081
GPT teacher head0.462
Teacher spread0.381 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations21
Published2018
Admission routes3
Has abstractyes

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