Linguistic and clinical validation of the Chinese version of the Acute Cystitis Symptom Score (ACSS) questionnaire in Taiwanese women with uncomplicated acute cystitis
Bibliographic record
Abstract
Purpose: The Acute Cystitis Symptom Score (ACSS) questionnaire is designed to diagnose acute cystitis in women. By quantifying the severity of symptoms, ACSS provides objective diagnostic criteria. The aim of this study is to translate the ACSS questionnaire into a Chinese version and validate its clinical use for diagnosing acute cystitis in Taiwanese women. Materials and methods: After rigorously translating the ACSS questionnaire into traditional Chinese used by the Mandarin-speaking Taiwanese people, it was clinically validated. Women aged 20 and above with suspected acute cystitis were recruited as the patients, and healthy women undergoing health check-ups as controls. Discriminative ability was assessed by comparing ACSS scores between the 2 groups, and the optimal diagnostic cutoff was determined using receiver operating characteristics analysis. In the patient group, treatment response was evaluated as patient-reported outcome by comparing ACSS scores pretreatment and posttreatment. Results: A total of 89 and 43 participants were recruited for the patient and control groups, respectively. The total score of typical symptoms between the patient and the control groups was significantly different ( P < 0.001). After antibiotic treatment, the total score of typical symptoms in the patient group significantly decreased ( P < 0.001). Using receiver operating characteristics curve analysis, the best cutoff score for diagnosing acute cystitis was 4, with a sensitivity and a specificity of 75.3% and 95.3%, respectively. Conclusion: After clinical validation, the Chinese version of the ACSS questionnaire can now be used as a symptom-oriented diagnostic and patient-reported outcome tool for acute cystitis in the Mandarin-speaking female population in Taiwan.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".