Accuracy of visual inspection with acetic acid in cervical ectopy evaluation
Bibliographic record
Abstract
Background: Cervical erosion is one of the most common pathological conditions encountered in outpatient gynecological clinics in middle-aged women. A simple method for diagnosis and treatment can play a tremendous role to comfort these women. Methods: The study includes fifty female patients attended the outpatient clinic of obstetrics & gynecology department in Mansoura University hospitals. All patients selected according to inclusion and exclusion criteria. The study was undertaken during the period from June 2014 to December 2014. Tools: Two tools were used for data collection: (1) Interviewing questionnaire schedule divided into three parts which were used to assess general characteristics of women, obstetric and gynecological history and the presence of cervical ectopy symptoms. (2) Local cervical assessment by vinegar acetic acid (VIA) test. Results: The study results revealed that 30 subjects (60%) were VIA positive and 20 subjects (40%) were VIA negative. VIA sensitivity was 100%, specificity 45%, PPV 71% and NPV 75%. There was a statistically significant relation between duration of marriage, high parity, IUDs use and VIA positivity. Conclusions: Using VIA test was an accurate and effective method for detection of cervical ectopy. VIA test should be performed in all the women attending outpatient gynecological clinics even in the presence of Pap smear facility to improve detection rate of cervical lesions and provide better patient counseling and treatment.
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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 | 0.013 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".