Tradition Versus Innovation: Conventional Radiography and Ultrasound in Calcium Pyrophosphate Deposition Identification. Instructions for Use
Bibliographic record
Abstract
OBJECTIVE: Conventional radiography (CR) and ultrasound (US) are used interchangeably for identification of calcium pyrophosphate deposition (CPPD). The aim of this study was to assess whether combining US and CR offers greater accuracy over either modality alone for the identification of CPPD. METHODS: Consecutive patients scheduled for knee replacement surgery for osteoarthritis were enrolled. Before surgery, patients underwent CR and US of the knee. Menisci and hyaline cartilage were collected and analyzed using polarized light microscopy to confirm the presence of CPPD (gold standard). CR and US were assessed for absence/presence of CPPD by expert radiologists and sonographers. Diagnostic performance statistics were calculated. Poisson models with robust variance estimators were used to determine the likelihood of identifying CPPD. RESULTS: Fifty-one patients (63% female, mean age 71.4 [SD 8] years) were enrolled. US demonstrated higher overall accuracy than CR for CPPD identification (0.78 vs 0.73). Sequential use of both modalities provided an advantage when only 1 knee site was positive in 1 of the 2 techniques; however, when 2 or 3 sites were positive, no additional advantage was observed. When US was negative, subsequent CR did not improve CPPD detection, but in cases of a negative CR, a positive US increased the likelihood of CPPD by 4.21 times, whereas a negative US substantially reduced the probability of CPPD, increasing the likelihood of its absence by 76%. CONCLUSION: US was more accurate than CR for identification of CPPD. Performing both exams can be an added value for CPPD identification only in a few specific cases.
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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 | 0.013 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".