Fibrinogen for the prediction of severe maternal complications in placental abruption with fetal death after 24 weeks of gestation
Bibliographic record
Abstract
OBJECTIVE: To assess the correlation between standard laboratory indicators at admission and severe maternal complications due to placental abruption (PA) with intrauterine fetal death (IUFD) after 24 weeks. METHODS: Retrospective study in three French tertiary referral hospitals. Correlation of laboratory indicators at admission (platelet count, prothrombin, activated partial thromboplastin time, fibrinogen) and severe maternal complications (massive transfusion, multiple organ failure, hysterectomy, or maternal deaths) in patients with PA and IUFD. RESULTS: Over 12 years, we identified 27/344 (7.8%) pregnant women presenting PA with IUFD. No patient had coagulopathy at admission. Fifteen individuals (55.5%) underwent delivery by cesarean section before or during labor. Fifteen individuals (55.5%) presented severe complications, and 17/27 (63%) lost more than 1 L of blood during delivery. Fibrinogen level was shown to be the laboratory indicator most correlated with severe complications (r = -0.52, P = 0.01). The receiver operating characteristic curve of fibrinogen less than 1.9 g/L in the prediction of severe complications (area under the curve = 0.80, 95% confidence interval [CI] 0.54-0.97) showed both a sensitivity and specificity of 83% (95% CI 54%-96%). CONCLUSIONS: In cases of IUFD with PA, fibrinogen levels at admission had a prognostic value for the prediction of severe maternal complications.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".