Frequency of common complications among patients presenting with septic induced abortion.
Bibliographic record
Abstract
Objectives: To find frequency of common complications among patients presenting with induced septic abortion attending OPD of Hayatabad Medical Complex Peshawar. Study Design: Descriptive cross-sectional study. Setting: Department of Obstetrics & Gynecology Hayatabad Medical complex Peshawar. Period: 6 October, 2016 to 6 April, 2017. Material & Methods: Through a cross sectional validation study design, 123 pregnant female patients with induced septic abortion having a gestation period of 20 to 22 weeks were included in the research study. After their consent, detailed clinical examination and history of patients were taken. SPSS version 10.0 was used for analysis of the collected data. Descriptive statistical data like mean + Standard deviation was measured for age, gravidity & parity. Likewise, frequency & percentage was calculated for hemorrhage, diffuse peritonitis and severe anemia. Results: As per Common Complications, frequencies and percentages for hemorrhage was recorded in 31 (25.20%) patients, diffuse peritonitis was recorded in 49 (39.83%) patients while severe anemia was recorded in 26 (21.13%) patients. However, 17 patients (13.82%) had no complications other than septic abortion. Conclusion: Our study concluded that the mishap of septic-induced abortion is totally preventable. The definitive commitment to women’s health can be achieved through effective contraception and by strengthening the family welfare services. Positive results can be achieved by discouraging repeated terminations of pregnancy.
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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 | 0.002 |
| 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 | 0.000 | 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".