Development of a placental specimen collection for use in an educational module correlating placental pathology and clinical outcomes (536.1)
Bibliographic record
Abstract
Understanding the anatomy of the placenta is essential in order to identify potential clinical problems during pregnancy. However, it is an organ that is difficult to study without a three‐dimensional appreciation of its structure. The aim of this study was to develop a collection of normal and abnormal plastinated placenta models and accompanying clinical and educational materials to provide information on anatomical abnormalities and their associated pregnancy outcomes. The placentas were plastinated using standard S10 silicone plastination and educational modules were developed, which included clinical information, ultrasound images, photographs and background information on the pregnancy outcomes. The plastinates were evaluated by a questionnaire distributed to undergraduate students at Queen’s University (n=16) and attendees at the 8th Annual Human Placenta Workshop (n=18). Data collected from the questionnaire included 76.5% of respondents rating the usefulness of the specimens an 8 out of 10 or greater and 100% of respondents that wished to have the plastinated placentas available for future learning opportunities. There was a positive response towards the use of plastinates as a supplement to the current methods used in teaching anatomy of the placenta. Plastinates are a valuable addition to teaching resources and students would benefit from the addition of plastinates as learning tools.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".