Classification of Cesarean Sections in Small Private Maternity Hospitals as assessed by the Modified Robson Criteria (Canada)
Bibliographic record
Abstract
ABSTRACT Aims To assess 15 years’ cesarean section data from small private maternity hospitals by the Modified Robson Criteria (Canada). To identify the groups that need to be focused to reduce the cesarean section rate. Materials and methods Classification of 7,342 cesarean section cases carried out over a period of 15 years from different small private maternity hospitals run by a single obstetrician was done using the Modified Robson Criteria (Canada). The contribution made by each group and subgroup was studied. Results About 50% of cesarean section cases occur in groups 1 and 2. The second largest group was group 5 (28.61%). A little over three-fourth of the contribution (78.12%) was made by nulliparous and previous cesarean section cases done at term with cephalic presentation. About one-tenth of the total cases belonged to the group of multiparous women. Conclusion The Modified Robson Criteria give us more clarity and allow perfect targeting. It is necessary to target group 1, 2B, and 5C to bring down the cesarean section rate in private maternity hospitals as the total of these subgroups makes it to little over 60%. How to cite this article Atnurkar KB, Mahale AR. Classification of Cesarean Sections in Small Private Maternity Hospitals as assessed by the Modified Robson Criteria (Canada). J South Asian Feder Obst Gynae 2016;8(2):107-112.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".