Factors determining success of the chronically instrumented unanesthetized fetal sheep model of human development: a retrospective cohort study
Bibliographic record
Abstract
Abstract Aim Chronically instrumented non-anesthetized fetal sheep (CINAFS) have been the mainstay model of human fetal development for 60 years. As a large “two for one” animal model, with instrumentation of the ewe and her fetus, the model poses challenges to implement de novo and to maintain overtime at the highest standards of operating procedures to ensure its ongoing performance. A common, yet conventionally underreported issue researchers face is the rate of animal loss. Here, we investigate what determines the success of the pregnant sheep model. Methods We conducted a retrospective cohort study consisting of 82 experiments spanning the course of six years. Our team identified ten variables that we anticipated were likely to influence the experimental outcome, such as the time of year, animal size, and surgical complexity. Results The single variable identified in this study as determining the successful outcome of the experiments is the experience level of the team. Conclusion The CINAFS model offers enormous potential to further our understanding of human fetal development and to create interventional technologies. However, to improve the outcomes of CINAFS models, improved 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. Key points The fetal sheep model closely mirrors the physiology of human fetal development In addition to its high translational potential, this model is known to have some generally not reported rate of experimental failure We show that factors such as animal characteristics & surgical complexity do not influence the experimental outcomes Instead, the key factor in model experimental success is the level of the research team’s experience The key factors to improve the animal model outcomes are an intra- and inter-team communication
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".