Development and Evaluation of the Arabic Index of Premature Ejaculation (AIPE)
Bibliographic record
Abstract
OBJECTIVES: Our report describes the construction and evaluation of the Arabic Index Premature Ejaculation (AIPE) as a diagnostic tool for premature ejaculation (PE) and presents data supporting its validity. METHODS AND MAIN OUTCOME MEASURES: Seventy-one men complaining of PE and 73 healthy subjects were asked to complete the seven-question AIPE. Diagnosis of PE was based on the criteria set by the second consultation on sexual dysfunctions. The seven items selected were based on assessment of erectile function, sexual desire, ejaculation latency, ejaculation control, patient satisfaction, partner satisfaction, and psychological distress. The AIPE was examined for sensitivity, specificity, and construct validity. RESULTS: A receiver operating characteristic curve indicated that the AIPE is an excellent diagnostic test. A cutoff score of 30 (range of scores 7-35) discriminated best (sensitivity = 0.98, specificity = 0.88). Severity of PE ranged from none (31-35) to severe (7-13). A high kappa value (0.85) indicated existence of significant agreement existed between the predicted and "true" PE classes. CONCLUSIONS: AIPE shows a potential to be a reliable aid to decrease the number of misdiagnosed cases of PE.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".