Reasons Why Some Japanese Pregnant Women Choose Trial of Labor After Cesarean
Bibliographic record
Abstract
BACKGROUND: We examined whether or not the Japanese pregnant women with a history of a cesarean section have the knowledge about the benefits and harms of trial of labor after cesarean (TOLAC) and elective repeat cesarean delivery (ERCD). METHODS: We reviewed the obstetric records of 121 Japanese women with a prior cesarean section who visited our hospital for reservation of their second delivery between January and December 2013. RESULTS: Forty-five (37%) of them wanted to perform TOLAC at the first interview. Of these, 14 women (31%) with a history of an urgent cesarean chose TOLAC because of the insufficient anesthetic effect during cesarean, while 11 women (24%) with a history of an elective cesarean did not have the knowledge of the risks of TOLAC and urgent cesarean. Nineteen of those (76%) selected ERCD following the counseling. CONCLUSIONS: Some Japanese pregnant women with TOLAC hope seemed to have insufficient knowledge about the benefits and harms of TOLAC and ERCD. Therefore, the improvement of the process of counseling and decision making may be needed for pregnant women with a history of a cesarean section in Japan.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".