CONGENITAL HYDROCEPHALUS- AN EPIDEMIOLOGICAL STUDY OF MATERNAL CHARACTERISTICS IN A TERTIARY CARE CENTRE
Bibliographic record
Abstract
BACKGROUNDThe epidemiology of congenital hydrocephalus is very unclear.Although various risk factors like maternal age, maternal illness and child factors have been studied in the past, still there is a gap of knowledge regarding maternal risk factors associated in congenital hydrocephalus in the literature.The objective of the study is to measure risk factor proportions of mothers among the population of a tertiary care centre. MATERIALS AND METHODSA record-based, retrospective, descriptive study with secondary data analysis was done from the medical case records of women who delivered babies with congenital hydrocephalus from Jan. 2006 to Dec. 2016.Maternal epidemiological characteristics such as maternal age, parity, place of residence, educational status as well as birth characteristics were studied and analysed by using appropriate statistical methods. RESULTSA total of 123 babies were born with congenital hydrocephalus during the study period.Most mothers in the study were second gravida (56.91%), rural based (74.79%), women between 21-30 years (80.48%) of age.Around 60.16% women were educated up to 10 th grade and most (41.46%) were homemakers.All but 5 women had vaginal births.Male babies were 52.03%, and 64.78% babies weighed above 2 kg at birth. CONCLUSIONYoung women from rural areas of low literacy and bearing their second babies are more prone to deliver babies with congenital hydrocephalus.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".