1141404284 How should data on murine spontaneous abortions be expressed and analysed?
Bibliographic record
Abstract
Problem: Spontaneous abortions (resorptions) in the CBAxDBA/2 are normally reported as number or resorptions/total number of implantations pooling >5 mice (R/T). The significance of differences between groups is 2 or Fisher's Exact test determined using non‐parametric methods (e.g. based on a priori predictions). Recently, it has been suggested that medians with box‐plots replace the accepted standard. This has led to reported rates of abortion of zero, where a median (that divides a population into the 50% of mice > the median and the 50% < the median) cannot logically exist. This method gives equal value to a mouse with 0/2 = 0% abortions and 0/10 = 0% abortions, and deprives readers of key data required to verify P values. The fact that there is an irreducible minimum loss due to chromosomally abnormal embryos (2–5%) is also ignored. Methods: Raw data on 177 individual CBAxDBA/2 matings was analysed by median, mean, and geometric mean, along with R/T from 6 independent experiments with 5–10 mice per group. Result: Individual abortion rates showed a non‐Gaussian distribution, but the mean and median differed by <0.5%. R/T data from the 6 independent experiments were normally distributed. Only mean and geometric mean enable between‐group P value calculation. Conclusion: Although it is possible to compare individual mice (and even individual implantation sites), as the relevant question is the significance of differenced between treatments in groups of mice and reproducibility, the established classical method should continue to be used.
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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 | 0.026 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.017 |
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".