OP26.11: Intrapartum quality Doppler profile predicting postpartum hemorrage
Bibliographic record
Abstract
Investigate the application of QDP in post partum hemorrage, studying DIR shape of uterine artery immediately after delivery we described the morphology of uterine flow profile comparing with the post partum quantity of blood losses. We enrolled consecutives women in labour, immediately after of placental delivery both uterine arteries where investigated with approximately 60° of incidence using QDP tool with DIR activated. After acquisition the frame was frozen at the systolic peak in both arteries and the profile shape shown with DIR. Using the Turbulence Index classification both arteries where assigned a value between 0 and 3, as 0 with maximum convexity and 3 maximum turbulence. Parity, gestational age, maternal age, previous Caesarean section, dosage and mode of induction, labour duration, operative or spontaneous vaginal delivery, blood losses, where recorded and analysed using non parametric test, Spearman r. We evaluate 236 women at 39 mean gestational age, 24,2% has induction of labour, 87.3% has spontaneous vaginal delivery and 12,7% has vacuum operative delivery, birthweight ranged between 1790 and 4330 kg, postpartum losses between 50 cc and 1800 cc, in 17 cases we have PPH (considered blood losses up and equal to 1000 cc) dividing in tree classes of blood losses: less than 500 cc; between 500 cc and 1000 cc and up to 1000 cc we found an inverse correlation between the turbulence index (TI) and blood losses [r:-0.651; p < 0.0001], also as expected birthweight [r:-0.156; p < 0.017], we do not found any correlation between the induction and the blood losses. We found a strict correlation between the turbulence index and the blood losses, in PPH and in non PPH. The use of TI is a promising tool for PPH prevention screening.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".