ANALYSIS OF CAESAREAN SECTION RATES USING THE ROBSON’S TEN GROUP CLASSIFICATION SYSTEM (TGCS) AT TERTIARY LEVEL HEALTHCARE FACILITIES IN RAWALPINDI, PAKISTAN: A CROSS-SECTIONAL STUDY
Bibliographic record
Abstract
Background: Robson’s Ten Group Classification System (TGCS) is recommended as a global standard for assessing, monitoring, and comparing Cesarean Section rates at all levels. This study was conducted to audit CS deliveries using the Robson TGCS to understand the current CS practices and analyze the groups of women who are mainly contributing to the rising rates of CS in Pakistan. Methods: A cross-sectional study was conducted in three tertiary care hospitals in Rawalpindi, Pakistan. All women who gave birth in these health facilities between, June to August 2019, were included in the study. Data were collected using a standardized proforma and analyzed using Robson guidelines to calculate each group’s relative size, group-specific CS rate, and relative and absolute group contributions toward overall Caesarean section rates. Results: A total of 5,657 deliveries were analyzed. Out of these, 2255 (40%) were Cesarean sections. Women in Group 3 made the largest contribution to the obstetric population accounting for 26.3% of all deliveries. The largest contributors to the overall CS rate were Group 5 (41.7 %), Group 10 (17.3%), and Group 2 (12.7%). Conclusion: A CS rate of 39.9% was reported, which is much higher than the WHO recommended optimal rate of CS. Group 5 (previous CS) was found to be the largest contributor to the overall CS rates followed by Group 10. This study provides a model for institutionalizing RTGCS and should be replicated in other districts of Pakistan.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".