FREQUENCY OF MATERNAL NEAR-MISS IN OBSTETRICAL & GYNECOLOGY UNIT OF A TERTIARY CARE HOSPITAL
Bibliographic record
Abstract
Objectives: To determine the frequency of near-miss among patients admitted during pregnancy till postpartum period.Materials and Methods: This cross-sectional study was conducted in Gynecology & Obstetrics Unit (B) of Lady Reading Hospital, Peshawar from 9/12/2019 to 9/6/2020. All women between age 15-48 years during pregnancy till 42 days after the end of pregnancy, admitted in the ward were included while a pregnant lady with complication not associated with pregnancy were excluded. Data on complications, mode of delivery, age and parity were taken from registers maintained in labor room, obstetrical ward and intensive care unit. Effect modifiers like age, gestational age. parity, gravida and causes were controlled through stratifications by using chi square test while P-value < 0.05 was taken as significant.Results: Out of total of 611 women were observed in which mean age was 29 ± 10.91 years. About 238(39%) patients were observed for primi para while 373(61%) patients were multi para. Similarly, 226(37%) patients were primi gravida while most of the patients were 385(63%) were multi gravida. Only 31(5%) patients had shock while 12(2%) patients had fits and very little number of 6(1%) patients had proteinuria. More over 8% patients had near miss while 92% patients didn’t had near miss.Conclusion: Our study concludes that the frequency of near-miss was 8% among patients admitted during pregnancy till postpartum period.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".