Relationship Between The Obstetric Profile Of Mothers And The Need For Newborn Resuscitation
Bibliographic record
Abstract
In most cases, the births in Brazil take place in a quiet way. However, as a transitional period susceptible to complications, the newborn may unexpectedly require resuscitation. Therefore, the presence of at least one trained professional in case of need to perform resuscitation is necessary. This study aimed to correlate the obstetric profile of mothers with neonatal resuscitation. The quantitative approach was used. Data were collected between February and March 2016, in a referral hospital in obstetrics, in a city of Cariri Region. To collect the data, we used a form that included questions related to the purpose of the study. After collection, the data were analyzed, and organized in Excel 2010 spreadsheets program, and presented through tables and graphs built in Word and Excel 2010 programs. We performed a Pearson correlation test with SPSS 22.0 program. With this study, it was concluded that there is a great relationship between the need for resuscitation maneuver and diseases during pregnancy, stock time and type of delivery. Also, deserves due attention to the statistical relationship between death and use of medication and meconium. The obstetric patient profile is important and must be considered in the clinical evolution of the newborn. Many of these factors can be identified even during pregnancy, during prenatal care, thus enabling the implementation of interventions in a timely manner.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".