A systematic review of database validation studies among fertility populations
Notice bibliographique
Résumé
STUDY QUESTION: Are routinely collected data from fertility populations adequately validated? SUMMARY ANSWER: Of the 19 studies included, only one validated a national fertility registry and none reported their results in accordance with recommended reporting guidelines for validation studies. WHAT IS KNOWN ALREADY: Routinely collected data, including administrative databases and registries, are excellent sources of data, particularly for reporting, quality assurance, and research. However, these data are subject to misclassification bias due to misdiagnosis or errors in data entry and therefore need to be validated prior to using for clinical or research purposes. STUDY DESIGN SIZE DURATION: We conducted a systematic review by searching Medline, Embase, and CINAHL from inception to 6 October 2016 to identify validation studies of databases that contain routinely collected data in an ART setting. Webpages of international ART centers were also searched. PARTICIPANTS/MATERIALS SETTING METHODS: We included studies that compared at least two data sources to validate ART population data. Key words and MeSH terms were adapted from previous systematic reviews investigating routinely collected data (e.g. administrative databases and registries), measures of validity (including sensitivity, specificity, and predictive value), and ART (including infertility, IVF, advanced reproductive age, and diminished ovarian reserve). Only full-text studies in English were considered. Results were synthesized qualitatively. The electronic search yielded 1074 citations, of which 19 met the inclusion criteria. MAIN RESULTS AND THE ROLE OF CHANCE: Two studies validated a fertility database using medical records; seven studies used an IVF registry to validate vital records or maternal questionnaires, and two studies failed to adequately describe their reference standard. Four studies investigated the validity of mode of conception from birth registries; two studies validated diagnoses or treatments in a fertility database; four studies validated a linkage algorithm between a fertility registry and another administrative database; one study created an algorithm in a single database to identify a patient population. Sensitivity was the most commonly reported measure of validity (12 studies), followed by specificity (9 studies). Only three studies reported four or more measures of validation, and five studies presented CIs for their estimates. The prevalence of the variable in the target population (pre-test prevalence) was reported in seven studies; however, only four of the studies had prevalence estimates from the study population (post-test prevalence) within a 2% range of the pre-test estimate. The post-test estimate was largely discrepant from the pre-test value in two studies. LIMITATIONS REASONS FOR CAUTION: The search strategy was limited to the studies and reports published in English, which may not capture validation studies from countries that do not speak English. Furthermore, only three specific fertility-based diagnostic variables (advanced reproductive age, diminished ovarian reserve, and chorionicity) were searched in Medline, Embase, and CINAHL. Consequently, published studies with other diagnoses or conditions relevant to infertility may not have been captured in our review. WIDER IMPLICATIONS OF THE FINDINGS: There is a paucity of literature on validation of routinely collected data from a fertility population. Furthermore, the prevalence of the markers that have been validated are not being presented, which can lead to biased estimates. Stakeholders rely on these data for monitoring outcomes of treatments and adverse events; therefore, it is essential to ascertain the accuracy of these databases and make the reports publicly available. STUDY FUNDING/COMPETING INTERESTS: This study was supported by Canadian Institutes of Health Research (CIHR) (FDN-148438). There are no competing interests for any of the authors. REGISTRATION NUMBER: International Prospective Register of Systematic Reviews ID: CRD42016048466.
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.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,073 | 0,280 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,012 | 0,011 |
| Bibliométrie | 0,023 | 0,028 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,006 | 0,008 |
| Science ouverte | 0,005 | 0,004 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».