Diagnostic Value of Amsel's Clinical Criteria for Diagnosis of Bacterial Vaginosis
Bibliographic record
Abstract
INTRODUCTION: Bacterial vaginosis (BV) is one of the most prevalent infections in women of reproductive age. Amsel's criteria and Nugent scoring system are among the most commonly used diagnostic methods. Although Nugent scoring system is considered the gold standard for diagnosing BV, it is time consuming and costly, and its interpretation needs lab equipment and experts. Hence, most physicians are inclined to use simpler clinical criteria that are yet accurate instead.The present study aimed to determine the diagnostic value of Amsel's criteria in diagnosing BV. MATERIALS & METHODS: This present study was conducted to validate diagnostic tests of BVin 120 married women in 2013. Amsel's criteria and Nugent scoring system were used to diagnose BV. Nugent scoring system was considered the gold standard and sensitivity, specificity, positive predictive value and negative predictive value of Amsel's criteria were compared with those of Nugent scoring system. RESULTS: Kappa coefficient was used to assess the diagnostic value of Nugent scoring system and Amsel's criteria. Kappa coefficient was found 0.8, which confirms the reliability of both diagnostic methods. McNemar test did not reveal a significant difference between Nugent scoring system and Amsel's criteria in terms of diagnosing BV. As compared to Nugent scoring system, Asmel's criteria enjoy sensitivity of 0.91, specificity of 0.91, positive predictive value of 0.86, negative predictive value of 0.94, and accuracy of 0.91. CONCLUSION: If lab equipment is not available for diagnosing BV, Amsel's criteria can be as good as Nugent scoring system at diagnosing this infection.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".