Assessing first-stage labor progression and its relationship to complications
Bibliographic record
Abstract
BACKGROUND: New labor curves have challenged the traditional understanding of the general pattern of dilation and descent in labor. They also revealed wide variation in the time to advance in dilation. An interval of arrest such as 4 hours did not fall beyond normal limits until dilation had reached 6 cm. Thus, the American College of Obstetricians and Gynecologists/Society for Maternal-Fetal Medicine first-stage arrest criteria, based in part on these findings, are applicable only in late labor. The wide range of time to dilate is unavoidable because cervical dilation has neither a precise nor direct relationship to time. Newer statistical techniques (multifactorial models) can improve precision by incorporating several factors that are related directly to labor progress. At each examination, the calculations adapt to the mother's current labor conditions. They produce a quantitative assessment that is expressed in percentiles. Low percentiles indicate potentially problematic labor progression. OBJECTIVE: The purpose of this study was to assess the relationship between first-stage labor progress- and labor-related complications with the use of 2 different assessment methods. The first method was based on arrest of dilation definitions. The other method used percentile rankings of dilation or station based on adaptive multifactorial models. STUDY DESIGN: We included all 4703 cephalic-presenting, term, singleton births with electronic fetal monitoring and cord gases at 2 academic community referral hospitals in 2012 and 2013. We assessed electronic data for route of delivery, all dilation and station examinations, newborn infant status, electronic fetal monitoring tracings, and cord blood gases. The labor-related complication groups included 272 women with cesarean delivery for first-stage arrest, 558 with cesarean delivery for fetal heart rate concerns, 178 with obstetric hemorrhage, and 237 with neonatal depression, which left 3004 women in the spontaneous vaginal birth group. Receiver operating characteristic curves were constructed for each assessment method by measurement of the sensitivity for each complication vs the false-positive rate in the normal reference group. RESULTS: The duration of arrest at ≥6 cm dilation showed poor levels of discrimination for the cesarean delivery interventions (area under the curve, 0.55-0.65; P < .01) and no significant relationship to hemorrhage or neonatal depression. The dilation and station percentiles showed high discrimination for the cesarean delivery-related outcomes (area under the curve, 0.78-0.93; P < .01) and low discrimination for the clinical outcomes of hemorrhage and neonatal depression (area under the curve, 0.58-0.61; P < .01). CONCLUSIONS: Duration of arrest of dilation at ≥6 cm showed little or no discrimination for any of the complications. In comparison, percentile rankings that were based on the adaptive multifactorial models showed much higher discrimination for cesarean delivery interventions and better, but low discrimination for hemorrhage. Adaptive multifactorial models present a different method to assess labor progress. Rather than "pass/fail" criteria that are applicable only to dilation in late labor, they produce percentile rankings, assess 2 essential processes for vaginal birth (dilation and descent), and can be applied from 3 cm onward. Given the limitations of labor-progress assessment based solely on the passage of time and because of the extreme variation in decision-making for cesarean delivery for labor disorders, the types of mathematic analyses that are described in this article are logical and promising steps to help standardize labor assessment.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".