Prophylaxis for ophthalmia neonatorum in Brazil: A <i>snapshot</i> using a multi-professional national survey
Bibliographic record
Abstract
BACKGROUND: Brazil is a large country with an elevated incidence of Chlamydiatrachomatis (CT) and Neisseriagonorrhoeae (NG) during pregnancy and variable access to health care. The objective of the study was to identify ophthalmia neonatorum prophylaxis practices in the country. METHODS: A prospective multidisciplinary survey was conducted using a closed social media group. Fifteen questions were developed after literature review. Specific content included categorization of respondents and practices such as type of medication, age at administration, occurrence of clinical and/or chemical conjunctivitis and microbiology identification. Questions were multiple choice, but some allowed written response. RESULTS: A total of 1.015 professionals responded, representing 24 states (92%) and 181 cities; mainly neonatologists (64%) and general pediatricians (21%). 96% of respondents reported performing prophylaxis at their institutions, mostly at birth or <1 h of life (99%), and regardless the mode of delivery (73%). Frequently used medications are: 1% silver nitrate (64%), 2.5% povidone iodine (18%) or 10% silver vitelinate (12%), with some regional variations. Occurrence of chemical conjunctivitis was stated by 58% of the respondents and microbiology identification was unusual. CONCLUSIONS: Ophthalmia neonatorum prophylaxis Brazil is almost universal and mainly performed by the use of anti-septic medications, with some regional variability. However, identification and treatment of CT and NG in both parents and newborns is not accomplished.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".