Prediction of Intraperitoneal Adhesions in Repeated Cesarean Deliveries with Stria Gravidarum Scoring System: A Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: The preoperative prediction of intraperitoneal adhesion (IPA) before repeated cesarean deliveries (CD), which are becoming more prevalent, is crucial for maternal health. AIM: The aim of the study was to preoperatively predict IPA in repeated CD with the stria gravidarum (SG) scoring system. METHODS: A total of 167 patients with at least one previous CD at or beyond 37 weeks of gestation were analyzed. Preoperative SG was calculated according to the Davey scoring system: 0-2 score were defined as mild SG (Group 1; n: 94, 56.2%), and 3-8 score were defined as severe SG (Group 2; n = 73, 43.8%). Preoperative previous cesarean incision features were evaluated according to the Vancouver scar scale. IPA was evaluated according to the Nair's and modified Nair's scoring systems. RESULTS: Parity, younger age at first pregnancy, higher body mass index, number of previous CDs, rate of scar symptoms, Nair's and the modified Nair's scores were statistically significant in Group 2 (P = 0.01; P = 0.04; P = 0.007; P = 0.004; P < 0.001; P = 0.007; P = 0.02, respectively). Davey score ≥3 and Vancouver score ≥4.5 were determined as the cut-off value to predict IPA (P = 0.1 and 0.07, respectively). According to multivariate analysis, both Davey and Vancouver scores are independent factors in predicting IPA (P = 0.02 and 0.04, respectively). CONCLUSION: Evaluating the SG score through the Davey score in women with a history of previous CD may assist in predicting IPA status before the planning of a subsequent surgery.
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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 | 0.001 |
| 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) | 0.001 | 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".