Deriving a Clinical Prediction Tool to Measure the Success of Labour Induction [12OP]
Bibliographic record
Abstract
INTRODUCTION: There is high-quality evidence supporting induction of labour (IOL) for a number of maternal and fetal indications. However, one fifth of inductions fail, requiring emergency cesarean deliveries. This has negative clinical, emotional and financial implications. Our objective was to derive and internally validate a clinical prediction tool to determine the success of IOL. METHODS: Data was extracted from electronic medical records of consecutive pregnant women who were induced between April 26, 2016 and December 31, 2016, at Mount Sinai Hospital (Toronto, Canada). A multivariable logistic regression model was developed using variables identified as predictors of successful IOL by literature review and expert opinion. Repeated K-fold cross-validation was used to internally validate the model. RESULTS: Of the 916 cases of IOL, 249 (27%) failed. The multivariable logistic regression model found maternal age, parity, pre-pregnancy weight, pre-pregnancy body mass index, weight at delivery and cervical dilation at time of induction as significant predictors of successful IOL. The prediction tool was well calibrated (Hosmer-Lemeshow χ2=6.42, P=.60) and demonstrated good discriminatory ability (area under the receiver-operating characteristic curve [AUROC], 0.79 [95% CI 0.76–0.82]). Internal validation of the model showed a similar discriminatory ability (AUROC, 0.77 [95% CI 0.68–0.85). CONCLUSION: We have derived and internally validated a clinical prediction tool for IOL in a large and diverse population. Once prospectively validated in other settings, this has potential for widespread use in clinical practice and research, as well as for enhancing patient experience and allocation of healthcare resources.
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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 | 0.028 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".