Bibliographic record
Abstract
INTRODUCTION: With increasing rates of cesarean sections throughout the country, the importance of preventing them has become a priority to all physicians. After the Consensus report by ACOG and the Society for MFM was published in March, 2014, we decided to utilize the recommendations in our labor and delivery department to attempt to reduce our Nulliparous, Term, Singleton, Vertex (NTSV) cesarean delivery rate over a one year time period. Our NTSV cesarean section rate in 2013 was 35%. Our benchmark was set at less than 24%. METHODS: 1) Recommendations and were made to the entire department regarding appropriate management of the first and second stages of labor. 2) Guidelines were set for elective inductions of labor between 39 0/7 weeks and 41 0/7 weeks to only allow those with bishop scores greater than 8. 3) Departmental and individual provider NTSV rates were shared with all physicians in an un-blinded fashion on a quarterly basis. RESULTS: Over a one-year time period we were able to reduce our NTSV cesarean section rates from 35% to 29.4%. Our second quarter of this year was 28.7%. CONCLUSION: While we have yet to reach our benchmark, we were able to decrease our NTSV cesarean section rates by over 5% in a one-year period. We continue to evaluate our procedures and educate our clinicians in an effort toward decreasing our NTSV cesarean section rate.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".