Using Ultrasound to Teach Female Reproductive Physiology Donald School Journal of Ultrasound in Obstetrics and Gynecology, October-December 2009;3(4):73-76 73 Using Ultrasound to Teach Female Reproductive
Bibliographic record
Abstract
Physiology ties together many related disciplines including anatomy, histology, biochemistry, and cell biology. Mastering the concepts of physiology is essential to understanding the principles of medicine. Learning physiology requires acquisition of facts, but this alone provides little in the way of useful knowledge. Currently, educators are challenged with the task of presenting physiology in a manner that encourages students to actively learn the required material. Didactic lectures were considered antiquated a century ago by Abraham Flexner when he reported on the status of medical education in the United States and Canada. Unfortunately, his comments did little to dissuade the use of lecture as a primary teaching method in medical education. Today we have the opportunity to develop new methods to present material in a manner that encourages active learning and understanding of concepts. Ultrasound imaging is a tool useful in presenting many organ systems in physiology. This is especially true of the female reproductive system. Ultrasound equipment can be used to develop still images of the ovaries, fallopian tubes, uterus, and the developing fetus. It can also provide video clips showing the reproductive organs in juxtaposition with the surrounding tissue or images of the fetus complete with heart sounds and vascular flow.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".