Pilot study on <sup>18</sup>F‐FDG PET/CT for detection of inflammatory changes in blood‐induced knee arthropathy in a rabbit model
Bibliographic record
Abstract
RATIONALE: F-FDG-PET/CT to detect inflammatory changes associated with blood-induced arthropathy in knees of a rabbit model. METHODS: Ten juvenile rabbits were imaged at baseline and weeks 5 and 17 post intraarticular autologous blood injections (ABI). Five rabbits in group 1 (G1) had ABI into the same knee joint every 2 weeks (total, eight injections). Five rabbits in group 2 (G2) had only two injections into the same knee, at weeks 5 and 17. Images were assessed visually and semi-quantitatively by measuring maximal standardized uptake values (SUVmax) and standardized uptake ratio (SUR = SUVmax in affected knee/SUVmax in non-affected knee). RESULTS: More rabbits in G1 than G2 presented with positive chronic inflammatory synovial scores at week 17. Mean iron staining scores in injected knees were greater for G1 than for G2 (P = 0.049). No increased uptake was identified in the injected knees in any of the rabbits at baseline or at week 5. At week 17, all G1 rabbits demonstrated increased uptake in their affected knees with higher mean SUVmax (1.5) than normal knees (1.0) (P < 0.02). None of the G2 rabbits showed asymmetric increased uptake. The SUR of G1 was higher at week 17 compared to baseline (P < 0.01) and week 5 (P < 0.01). The SUR at week 17 was higher for G1 than for G2 (1.13) rabbits (P < 0.01). CONCLUSION: F-FDG-PET is able to detect the inflammatory changes associated with haemophilic arthropathy in this experimental model.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".