Factors Determining the Success of the Chronically Instrumented Non-anesthetized Fetal Sheep Model of Human Development: A Retrospective Cohort Study
Bibliographic record
Abstract
Background The chronically instrumented non-anesthetized fetal sheep (CINAFS) model has been a mainstay of human fetal development research for the past 60 years. As a large "two for one" animal model, involving the instrumentation of the ewe and her fetus, the model poses challenges to implement de novo and maintain overtime at the highest standards of operating procedures to ensure ongoing performance. A common yet conventionally underreported issue researchers face is a high rate of animal loss. Here, we investigate what determines the success of the CINAFS model of human development. Methods We conducted a retrospective cohort analysis consisting of 82 experiments spanning the course of six years. Our team identified 10 variables that we anticipated were likely to influence the experimental outcome, such as the time of year, animal size, and surgical complexity. To evaluate the role of each variable in contributing to the success of the model, a binary logit regression analysis with a Fisher scoring optimization was fit to the data (SAS, V9 engine, release 3.8, SAS Institute, Cary, NC, USA). A higher predictive probability indicates a larger impact by the given variable on the outcome of the experiment. A Wald chi-squared analysis was run on the data to control for confounders and determine significance. Results The single variable identified in this study as determining the success of experiment outcomes using the CINAFS model is the experience level of the team. Conclusion The CINAFS model offers enormous potential to further our understanding of human fetal development and create interventional technologies related to fetal health. However, to improve experimental outcomes using the CINAFS model, stronger communication and training are needed. We discuss the implications of our findings for the successful implementation of this challenging yet scientifically advantageous animal model of human physiology.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".