Postpartum X-ray pelvimetry. Its use in calculating the fetal-pelvic index and predicting fetal-pelvic disproportion.
Bibliographic record
Abstract
OBJECTIVE: To determine whether postpartum x-ray pelvimetry can be used to calculate the fetal-pelvic index (FPI) in future pregnancies. STUDY DESIGN: In stage I of the study, 10 gravid women, after 36 completed weeks' gestation, underwent x-ray pelvimetry before delivery. Pelvimetry was repeated within two days after delivery. Comparisons between antepartum and postpartum measurements were made using paired t tests and correlation coefficients. In stage II, 25 gravid women, after 36 completed weeks' gestation, underwent fetal ultrasound for biometry. X-ray pelvimetry was performed within two days after delivery. FPI was calculated for each pregnancy using antepartum fetal ultrasound and postpartum pelvimetry measurements. FPI calculations were correlated with the incidence of fetal-pelvic disproportion (FPD), as indicated by the requirement for cesarean section for arrest of active labor. Sensitivity, specificity and predictive value of FPI were assessed. RESULTS: In stage I, mean anteroposterior and transverse diameters of the pelvic inlet, midpelvis and pelvic outlet did not differ significantly. In stage II, the sensitivity of FPI for detecting FPD was 100%, specificity 95%, positive predictive value 80%, and negative predictive value 100%. CONCLUSION: Postpartum pelvimetry has the same association with FPD as antepartum pelvimetry. The strategy of using postpartum pelvimetry and antepartum fetal biometry to calculate FPI successfully identified 100% of the patients who ultimately required cesarean section for FPD, with a false positive rate of 5%. Pelvimetry performed postpartum in an index pregnancy may be used in future pregnancies, in combination with antepartum fetal ultrasound, to calculate FPI and predict the likelihood of FPD.
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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 | 0.000 | 0.000 |
| 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".