Diagnosing onset of labor: a systematic review of definitions in the research literature
Bibliographic record
Abstract
BACKGROUND: The diagnosis of labor onset has been described as one of the most important judgments in maternity care. There is compelling evidence that the duration of both latent and active phase labor are clinically important and require consistent approaches to measurement. In order to measure the duration of labor phases systematically, we need standard definitions of their onset. We reviewed the literature to examine definitions of labor onset and the evidentiary basis provided for these definitions. METHODS: Five electronic databases were searched using predefined search terms. We included English, French and German language studies published between January 1978 and March 2014 defining the onset of latent labor and/or active labor in a population of healthy women with term births. Studies focusing exclusively on induced labor were excluded. RESULTS: We included 62 studies. Four 'types' of labor onset were defined: latent phase, active phase, first stage and unspecified. Labor onset was most commonly defined through the presence of regular painful contractions (71% of studies) and/or some measure of cervical dilatation (68% of studies). However, there was considerable discrepancy about what constituted onset of labor even within 'type' of labor onset. The majority of studies did not provide evidentiary support for their choice of definition of labor onset. CONCLUSIONS: There is little consensus regarding definitions of labor onset in the research literature. In order to avoid misdiagnosis of the onset of labor and identify departures from normal labor trajectories, a consistent and measurable definition of labor onset for each phase and stage is essential. In choosing standard definitions, the consequences of their use on rates of maternal and fetal morbidity must also be examined.
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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 | 0.031 | 0.146 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.037 | 0.032 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".