Investigator’s influence on the muscle strength assessment in animals in experiment: Comparison of automated “inverted grid” test and its classical variant
Bibliographic record
Abstract
The aim of the work was to study the influence of the researcher on the muscle strength assessment in animals in the experiment by comparing the results of the automated “inverted grid” test and its classical variant. Materials and methods. Male lines (Bla/J, n=20; FUS(1-359), n=20; Tau P301S+/+, n=20) and their background controls (C57BL/6J, n=20; CD1, n=20) were selected for the study. The dynamics of changes in the muscle deficit of the animals was evaluated in the automated and classical variant of the “inverted grid” test. Results. According to the results of the muscle strength assessment of mice with an edited genome of lines FUS(1-359)+/-, Tau P301S+/+, B6.ADysfprmd/GeneJ, using the “inverted grid” test in the classical variant and the automated one, it was found that statistically significant differences were not obtained in comparison with the results obtained by the classical variant of the test. The standard error of the mean increases by 23–39% in the classical test compared to the automated one. It was shown that the standard error of the mean in the classical variant of the test in Tau P301S+/+ mice was 6.24; 5.94; 5.88; 7.38 at 4 age points; in FUS(1-359)+/- mice, 4.49; 6.8; 6.98 and 4.1; B6.ADysfprmd/GeneJ mice, 7.66; 7.58; 8.3 and 7.92, respectively. Conclusion. Thus, the value of the standard error of the results study mean of the changes dynamics in the muscle strength when using the automated variant of the “inverted grid” test was reduced in comparison with the results of the classical variant of the test. The results of the study show that the automation of generally recognized behavioral tests is able to increase the accuracy of the obtained data reducing the influence of a human factor on the manipulation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".