Cyst‐like lesions in finger joints detected by conventional radiography: Comparison with 320‐row multidetector computed tomography
Bibliographic record
Abstract
OBJECTIVE: Many rheumatologists and radiologists routinely assess conventional radiographs of the hands, and it is often unclear how to proceed if radiography reveals only cyst-like lesions (CLLs), with otherwise normal findings. The present study was undertaken to evaluate the use of 320-row multidetector computed tomography (MDCT) of the hands in the further assessment of CLLs of metacarpophalangeal (MCP) and proximal interphalangeal (PIP) joints identified on conventional radiography. METHODS: MCP and PIP joints (n = 1,120 joints) of 56 consecutive patients (44 women [mean age 55 years, range 31-72 years] and 12 men [mean age 57 years, range 37-77 years]) were prospectively scored for the presence of cysts, CLLs, and erosions of the PIP and MCP joints, first on conventional radiographs, then on MDCT. Scoring was performed by 2 independent readers under blinded conditions. Intraclass correlation coefficients were calculated. RESULTS: By conventional hand radiography, 13 patients (total of 260 joints assessed) were identified as having CLLs in 1 or more joints (total of 36 joints [11 PIP and 25 MCP]). By MDCT, the findings in 19 of 36 joints (53%) were diagnosed as erosions, while 7 of 36 (19%) were confirmed as true cysts, and 10 joints (28%) were normal (false positive). Among the patients with CLLs, 10 of 224 joints with no abnormality seen radiographically had erosions as seen on MDCT. Interreader agreement for erosions was 0.854 (95% confidence interval [95% CI] 0.831-0.874) by conventional hand radiography and 0.952 (95% CI 0.943-0.959) by MDCT. CONCLUSION: Our results indicate that radiographic appearance of cyst-like lesions may actually represent erosions and should lead to initiation of further imaging tests.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".