Improving standard practices in studies using results from basic human semen examination
Bibliographic record
Abstract
The purpose of this article is to provide an explanation of the background behind a checklist that declares the laboratory methods used in a scientific study. It focuses primarily on implementing laboratory procedures to yield reliable results in basic semen examinations. While the World Health Organization (WHO) and international standards provide recommendations for basic semen examination, manuscripts submitted to Andrology frequently lack transparency regarding the specific techniques used. In addition, the terminology used for semen examination results often fails to provide a clear definition of the groups under study. Furthermore, the WHO's reference limits are often misinterpreted as strict boundaries between fertility and infertility. It is important to note that valid clinical andrological diagnoses and treatments cannot rely solely on semen examination results; they require proper laboratory procedures as a foundation for diagnosing and treating male patients. Therefore, scientific journals should promote the adoption of robust laboratory practices and an accurate definition of patient groups. A checklist can facilitate the design of high-quality studies and the creation of informative publications. Further, it can help journals assess submitted manuscripts and improve the overall quality of their publications.
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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 | 0.720 | 0.765 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.025 | 0.016 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.010 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".