Histological and reference system for the analysis of mouse intervertebral disc
Bibliographic record
Abstract
A new scoring system based on histo-morphology of mouse intervertebral disc (IVD) was established to assess changes in different mouse models of IVD degeneration and repair. IVDs from mouse strains of different ages, transgenic mice, or models of artificially induced IVD degeneration were assessed. Morphological features consistently observed in normal, and early/later stages of degeneration were categorized into a scoring system focused on nucleus pulposus (NP) and annulus fibrosus (AF) changes. "Normal NP" exhibited a highly cellularized cell mass that decreased with natural ageing and in disc degeneration. "Normal AF" consisted of distinct concentric lamellar structures, which was disrupted in severe degeneration. NP/AF clefts indicated more severe changes. Consistent scores were obtained between experienced and new users. Altogether, our scoring system effectively differentiated IVD changes in various strains of wild-type and genetically modified mice and in induced models of IVD degeneration, and is applicable from the post-natal stage to the aged mouse. This scoring tool and reference resource addresses a pressing need in the field for studying IVD changes and cross-study comparisons in mice, and facilitates a means to normalize mouse IVD assessment between different laboratories. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:233-243, 2018.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".