ORIGINAL ARTICLE: How Should Data on Murine Spontaneous Abortion Rates be Expressed and Analyzed?
Bibliographic record
Abstract
PROBLEM: Spontaneous abortions in the CBA x DBA/2 model are normally reported as number of resorptions/total number of implantations (R/T), pooling data from individual mice. The significance of differences between groups has been determined using non-parametric statistics (e.g. chi-square or Fisher's Exact test) based on a priori predictions. Recently, it has been argued that medians with box plots should replace the accepted standard, but this deprives readers of data needed to verify P-values, and leads to inferences incompatible with biological and statistical reality. METHOD OF STUDY: Raw data on 173 individual CBA x DBA/2 matings were analyzed by median and mean, along with R/T data from 18 independent experiments containing 5-10 mice per group. Raw data from 19 CBA x BALB/c matings were similarly analyzed. RESULTS: Individual CBA x DBA/2 mouse resorption rates showed a non-Gaussian distribution, but the mean and median differed by <0.5%. Resorption data from 6 and 12 independent pools of mice were normally distributed. Only the mean enabled a between-group P-value calculation. CBA x BALB/c matings gave a median of 0 and mean of 5.1%; the data were not normally distributed, but that was because of a bimodal distribution. One group of mice had 0 abortions, and the second a mean of 13.9% abortions, and the data from the latter group were normally distributed. CONCLUSION: Although it is possible to compare individual mice, and even individual implantation sites, in resorption (abortion) studies, as the relevant question is the significance of differences between treatment groups of mice, and reproducibility, the established classical method of reporting R/T should continue to be provided. In CBA x BALB/c matings, where abortion rates are low, using the median is misleading and may obscure the existence of two distinct populations.
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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 | 0.047 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".