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Enregistrement W2197831191 · doi:10.1093/humrep/dev305

‘How to count sperm properly’: checklist for acceptability of studies based on human semen analysis

2015· article· en· W2197831191 sur OpenAlex

Pourquoi ce travail est dans la base

Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.

affAu moins un auteur déclare une institution canadienne dans l'instantané OpenAlex épinglé.

Notice bibliographique

RevueHuman Reproduction · 2015
Typearticle
Langueen
DomaineMedicine
ThématiqueSperm and Testicular Function
Établissements canadiensAchieve Life Sciences (Canada)
Organismes subventionnairesnon disponible
Mots-clésSemenChecklistSemen analysisSpermAndrologyGynecologyMedicineBiologyInfertilityPregnancyGenetics

Résumé

récupéré en direct d'OpenAlex

STUDY QUESTION: Can a tool be developed for authors, reviewers and editors of the ESHRE Journals to improve the quality of published studies which rely on semen analysis data? SUMMARY ANSWER: A basic checklist for authors, reviewers and editors has been developed and is presented. WHAT IS KNOWN ALREADY: Laboratory work which includes semen analysis is burdened by a lack of standardization. This has significant negative effects on the quality of scientific and epidemiological studies, potential misclassification of patients and the potential to impair clinical treatments/diagnoses that rely on accurate semen quality information. Robust methods are available to reduce laboratory error in semen analysis, inducing adherence to World Health Organization techniques, participation in an external quality control scheme and appropriate training of laboratory personnel. However, journals have not had appropriate systems to assess if these methods have been used. STUDY DESIGN, SIZE, DURATION: After discussion at a series of Associate Editor Meetings of the ESHRE Journals the authors of the present text were asked to propose a tool for authors, reviewers and editors of the ESHRE Journals to ensure a high quality assessment of submitted manuscripts which rely on semen analysis data, including a detailed verification of the relevance and the quality of the methods used. PARTICIPANTS/MATERIALS, SETTING, METHODS: N/A. MAIN RESULTS AND THE ROLE OF CHANCE: A basic checklist for authors, reviewers and editors is presented. The checklist contains key points which should be considered by authors when designing studies and which provides essential information for when the submitted manuscript is evaluated. For published articles the answers in the checklist are suitable to be available as supplementary data, which will also reduce the space necessary for technical details in the printed article. LIMITATIONS, REASONS FOR CAUTION: Guidelines such as these should not be used uncritically. It is therefore important that submitting authors, in situations where their study does not comply with the basic requirements for semen analysis, not only explain all methodological deviations but also declare the level of uncertainty in their analyses and how it complies with, or might confound, the aims of the study. WIDER IMPLICATIONS OF THE FINDINGS: The fundamental importance of appropriate and robust methodology to facilitate advances in scientific understanding and patient management and treatment, is now accepted as being paramount. Use of the semen analysis checklist should be part of this process, and when completed and signed by the corresponding author at the time of submitting a manuscript should result in greater transparency, and ultimately uniformity. It is hoped that this initiative will pave the way for wider adoption of the methodology/reporting by other biomedical, epidemiological and scientific journals, and ultimately become the standard of practice for papers reporting semen analysis results obtained in laboratory and clinical andrology. Systems to assist referees, authors and editors to present high quality findings should have a significant impact on the field of reproductive medicine. STUDY FUNDING/COMPETING INTERESTS: No funding was obtained for this work. The authors have no competing interests in relation to the present publication and checklist. TRIAL REGISTRATION NUMBER: N/A.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,265
Score d'incertitude au seuil0,479

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,146
Tête enseignante GPT0,381
Écart entre enseignants0,235 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle