Development and internal validation of a Nomogram for preoperative prediction of surgical treatment effect on cesarean section diverticulum
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to develop and validate an individualized score based on preoperative parameters to predict patient outcomes after vaginal repair of cesarean section diverticulum. METHODS: This is a retrospective cohort study (Canadian Task Force classification II-2). Patients were enrolled between Jun 11, 2012, to May 27, 2016. Multivariable logistic regression analyses were used to construct the predictive model. Then, we generated a nomogram to assess the individualized risk of poor prognosis after operation. This prediction model included information from 167 eligible patients diagnosed with cesarean section diverticulum who underwent vaginal repair. Class-A healing group was defined as CSD patients who had menstruation duration of no more than 7 days and a thickness of the remaining muscular layer of no less than 5.8 mm after vaginal repair according to conferences. Others were included in the non-class-A healing group. A final nomogram was computed using a multivariable logistic regression model. RESULTS: The factors contained in the individualized prediction nomogram included the depth/ the thickness of the remaining muscular layer ratio, number of menstruation days before surgery, White blood cell and fibrinogen. This model demonstrated adequate discrimination and calibration (C-index = 0.718). There was a significant difference in the number of postmenstrual spotting days (12.98 ± 3.86 VS 14.46 ± 2.86, P = 0.022) and depth/ the thickness of the remaining muscular layer ratio (2.81 ± 1.54 VS 4.00 ± 3.09, P = 0.001) between two groups. Decision curve analysis showed that this nomogram was clinically useful. CONCLUSIONS: This cesarean section diverticulum score can predict the outcomes of cesarean section diverticulum and can be useful for counseling patients who are making treatment decisions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".