Evaluation of Clinical Measures and Different Criteria for Diagnosis of Adult-onset Still’s Disease in a Chinese Population
Bibliographic record
Abstract
OBJECTIVE: To determine the value of clinical measures in diagnosis of adult-onset Still's disease (AOSD), and to identify the optimal set of proposed classification criteria, in a Chinese population. METHODS: A total of 70 patients with AOSD and 140 non-AOSD inpatients with fever were retrospectively identified at Zhongshan Hospital, Shanghai, from January 2003 to December 2009. Clinical measures and 4 sets of diagnostic criteria (Yamaguchi, Calabro, Cush, and Reginato) were evaluated by sensitivity, specificity, positive/negative predictive value (PPV, NPV), and positive/negative likelihood ratio (PLR, NLR) for diagnosis of AOSD. RESULTS: In our series, higher sensitivity included hyperpyrexia (temperature ≥ 39°C, 94.29%), arthralgia (80.0%), polymorphonuclear neutrophils (PMN) ≥ 75% (84.29%), serum ferritin ≥ 2-fold the upper normal value (90.0%), negative antinuclear antibodies (85.29%), and rheumatoid factor (84.38%); while higher specificity included transient erythema (98.57%), sore throat (85.0%), leukocytes ≥ 15,000/mm(3) (87.86%), and PMN ≥ 85% (85.0%). Rash, arthralgia, and sore throat were found to have better sensitivity and specificity (PLR 3.29-4.86). Leukocytes ≥ 10,000/mm(3), PMN ≥ 80%, and serum ferritin ≥ 5-fold the upper normal limit were set as critical points. The Reginato criteria set had the highest specificity, 99.29%. The Yamaguchi set had the highest sensitivity, 78.57%, with a better accuracy of 87.14%. CONCLUSION: The Yamaguchi diagnostic criteria had better accuracy in Chinese patients. Indicators such as rash, arthralgia, sore throat, leukocytes ≥ 10,000/mm(3), PMN ≥ 80%, and serum ferritin ≥ 5-fold the upper normal limit were helpful for diagnosis of AOSD. We recommend using these indicators in combination instead of alone.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".