A Study of Various Factors Influencing Fetal Scalp Lactate and Their Correlation With Composite Fetal and Neonatal Outcomes
Bibliographic record
Abstract
Background: Fetal scalp lactate has been shown to be as effective as fetal scalp pH in predicting neonatal outcomes. Maternal-fetal factors influencing variability in fetal scalp lactate have not been fully explored. This study aims to explore the association of gestational age with fetal scalp lactate and examine whether existing thresholds are predictive of adverse outcomes. Methods: A retrospective study of all singleton births with a fetal scalp lactate taken during labor at a public teaching hospital between July 1, 2007 and June 1, 2013 was performed. Descriptive, bivariate and multivariate analysis was used to explore the association between fetal scalp lactate and other variables of interest. Results: A total of 326 patients with fetal scalp lactate values during labor were studied. Fetal scalp lactate was not associated with gestational age (Spearman’s rho = -0.006, P = 0.92). A fetal scalp lactate ≥ 4.8 mmol/L was associated with maternal age (P = 0.049) and time in labor (P = 0.001). Fetal scalp lactate was strongly associated with a combined outcome variable that included emergency operative delivery (OR = 1.90; 95% CI: 1.44 - 2.51; P < 0.001). There was no significant association with a poor combined fetal and neonatal outcome when emergency intervention was excluded (OR = 1.11; 95% CI: 0.93 - 1.25; P = 0.092). Conclusions: There was no significant correlation between fetal scalp lactate and gestational age; further exploration of the association with maternal age is warranted. A raised fetal scalp lactate is associated with progression to emergency operative delivery. J Clin Gynecol Obstet. 2015;4(2):212-216 doi: http://dx.doi.org/10.14740/jcgo329w Normal 0 false false false EN-AU X-NONE X-NONE
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".