Validation and reproducibility of computerised cell-viability analysis of tissue slices
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: The identification of live cells using membrane integrity dyes has become a frequently used technique, especially with articular cartilage and chondrocytes in situ where tissue slices are used to assess cell recovery as a function of location. The development of a reproducible computerised method of cell evaluation would eliminate many variables associated with manual counting and significantly reduce the amount of time required to evaluate experimental results. METHODS: To validate a custom computerised counting program, intra-person and inter-person cell counts of nine human evaluators (three groups - unskilled, novice, and experienced) were compared with repeated pixel counts of the custom program on 15 digitised images (in triplicate) of chondrocytes in situ stained with fluorescent dyes. RESULTS: Results indicated increased reproducibility with increased experience within evaluators [Intraclass Correlation Coefficient (ICC) range = 0.67 (unskilled) to 0.99 (experienced)] and between evaluators [ICC = 0.47 (unskilled), 0.85 (novice), 0.93 (experienced)]. The computer program had perfect reproducibility (ICC = 1.0). There was a significant relationship between the average of the experienced evaluators results and the custom program results (ICC = 0.77). CONCLUSIONS: This study demonstrated that increased experience in cell counting resulted in increased reproducibility both within and between human evaluators but confirmed that the computer program was the most reproducible. There was a good correlation between the intact cell recovery determined by the computer program and the experienced human evaluators. The results of this study showed that the computer counting program was a reproducible tool to evaluate intact cell recovery after use of membrane integrity dyes on chondrocytes in situ. This and the significant decrease in the time used to count the cells by the computer program advocate its use in future studies because it has significant advantages.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it