Variable placental thickness affects placental functional efficiency independent of other placental shape abnormalities
Bibliographic record
Abstract
Our previous work suggests that stressors that impact placental vascular growth result in a deformed chorionic surface shape, which reflects an abnormal placental three-dimensional shape. We propose to use variability of placental disk thickness as a reflector of deviations in placental vascular growth at the finer level of the fetal stems. We hypothesize that increased variability of thickness is associated with abnormal chorionic surface shape, but will be a predictor of reduced placental functional efficiency (smaller baby for a given placental weight) independent of shape. These measures may shed light on the mechanisms linking placental growth to risk of adult disease. The sample was drawn from the Pregnancy, Infection and Nutrition Study. In all, 94.6% of the cohort consented to placental examination. Of the 1023 delivered at term, those previously sectioned by the Pathology Department were excluded, leaving 587 (57%) cases with intact placentas that were sliced and photographed. The chorionic surface shape and the shape of a central randomly oriented placental slice were analyzed and measures were compared using correlation. Lower mean placental disk thickness and more variable disk thickness were each strongly and significantly correlated with deformed chorionic plate shapes. More variable disk thickness was strongly correlated with reduced placental efficiency independent of abnormal chorionic surface shape. Variability of placental disk thickness, simple to measure in a single randomly oriented central slice, may be an easily acquired measure that is an independent indicator of lowered placental efficiency, which may in turn program the infant and result in increased risk for development of adult diseases.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".