Proposal of guidelines for the appraisal of SEMen QUAlity studies (SEMQUA)
Bibliographic record
Abstract
STUDY QUESTION: Is there a need for a specific guide addressing studies of seminal quality? SUMMARY ANSWER: The proposed guidelines for the appraisal of SEMinal QUAlity studies (SEMQUA) reflect the need for improvement in methodology and research on semen quality. WHAT IS KNOWN ALREADY: From an examination of other instruments used to assess the quality of diagnostic studies, there was no guideline on studies of seminal quality. STUDY DESIGN, SIZE AND DURATION: Through systematic bibliographic search, potential items were identified and grouped into four blocks: participants, analytical methods, statistical methods and results. PARTICIPANTS/MATERIALS, SETTING AND METHODS: Our findings were presented to a panel of experts who were asked to identify opportunities for improvement. Then, a checklist was designed containing the questions generated by the items that summarize the essential points that need to be considered for the successful outcome of a SEMQUA. MAIN RESULTS AND THE ROLE OF CHANCE: Eighteen items were identified, from which 19 questions, grouped into four blocks, were generated to constitute the final checklist. An explanation for the inclusion of each item was provided and some examples found in the bibliographic search were cited. LIMITATIONS AND REASONS FOR CAUTION: We consider that not all items are equally applicable to all study designs, and so the hypothetical results are not comparable. For that reason, a score would not be fair to critically appraise a study. This checklist is presented as an instrument for appraising SEMQUAs and therefore remains open to constructive criticism. It will be further developed in the future, in parallel with the continuing evolution of SEMQUAs. WIDER IMPLICATIONS OF THE FINDINGS: The final configuration of the SEMQUA is in the form of a checklist, and includes the items generally considered to be essential for the proper development of a SEMQUA. The final checklist produced has various areas of application; for example, it would be useful for designing and constructing a SEMQUA, for reviewing a paper on the question, for educational purposes or as an instrument for appraising the quality of research articles in this field. STUDY FUNDING/COMPETING INTEREST(S): None.
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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 | 0.671 | 0.781 |
| Meta-epidemiology (narrow) | 0.008 | 0.011 |
| Meta-epidemiology (broad) | 0.016 | 0.024 |
| Bibliometrics | 0.042 | 0.031 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.030 | 0.021 |
| Open science | 0.025 | 0.025 |
| Research integrity | 0.027 | 0.039 |
| Insufficient payload (model declined to judge) | 0.014 | 0.017 |
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".