Factors That Influence Reliability of the Mouse Clinical Frailty Index
Bibliographic record
Abstract
Dear Editor, We thank Kane and colleagues for their careful attention to our report recently published in the Journal of Gerontology Biological Sciences (1), and for the opportunity to provide some additional description of our study. In their letter, they noted that, without a special procedure for assessing reliability, and without feedback of the results, they saw no trend toward increasing inter-rater reliability in their series, which included both the long-lived C57BL/6JJ mice and the short-lived DBA/2J mouse model. By contrast, both their recently published report (2) and our original study (1), which each included attempts to improve reliability, yielded better results. Kane and colleagues also call to attention that the professional background of the rater may be important. In their data, scientists and trainees showed greater inter-rater reliability than did animal technicians. Our data appear to support the former conclusion; without data from technicians we must be silent in the latter. The two who did the ratings in our original submission (H.F. and M.S.) were both graduate students at the time, with 4–5 years of experience in the research laboratory. Importantly, they achieved a high level of inter-rater reliability (intra-class correlation coefficient = 0.77; 95% confidence interval = 0.67–0.83) by the final set of ratings. The work of Kane and colleagues also strongly suggests that it is the professional background of the rater rather than the number of years of experience that are important. We cannot comment on this based on our study (1), as both raters in our study had similar backgrounds and years of experience.
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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 | 0.008 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".