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Enregistrement W3161671472 · doi:10.1016/j.xjon.2021.05.004

Commentary: One system to rule them all

2021· editorial· en· W3161671472 sur OpenAlexaboutno aff
Valerie X. Du, Shawn S. Groth

Notice bibliographique

RevueJTCVS Open · 2021
Typeeditorial
Langueen
DomaineMedicine
ThématiqueCardiac, Anesthesia and Surgical Outcomes
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésScopusMedicineHarmonizationMEDLINEAdverse effectCardiothoracic surgeryEsophagectomyCitationDatabaseSurgeryLibrary sciencePolitical scienceCancerEsophageal cancerComputer scienceInternal medicine

Résumé

récupéré en direct d'OpenAlex

Central MessageLack of consistent definitions for adverse events contributes to challenges comparing outcomes and combining information across large clinical databases; harmonization of these definitions is needed.See Article page 250. Lack of consistent definitions for adverse events contributes to challenges comparing outcomes and combining information across large clinical databases; harmonization of these definitions is needed. See Article page 250. Over the past 2 decades, the use of large, national clinical registries and administrative datasets has played an increasingly important role in thoracic surgery health services research.1Groth S.S. Habermann E.B. Massarweh N.N. United States Administrative Databases and Cancer Registries for Thoracic Surgery Health Services Research.Ann Thorac Surg. 2020; 109: 636-644Abstract Full Text Full Text PDF PubMed Scopus (9) Google Scholar In particular, postoperative complications (or adverse events [AEs]) are important metrics used in quality improvement initiatives and have implications on patient-centered outcomes.2Tevis S.E. Kennedy G.D. Postoperative complications and implications on patient-centered outcomes.J Surg Res. 2013; 181: 106-113Abstract Full Text Full Text PDF PubMed Scopus (61) Google Scholar However, the various database entities, such as the Society of Thoracic Surgeons (STS), European Society of Thoracic Surgeons (ESTS), the Esophagectomy Complications Consensus Group, the National Surgical Quality Improvement Program, and the Thoracic Morbidity and Mortality, which was adopted by the Canadian Association of Thoracic Surgeons, have unique definitions for and classifications of AEs. This longstanding problem of a lack of uniform and consistent definitions for AEs contributes to challenges when attempting to compare outcomes and combine information across these datasets. In this issue of the Journal, Sigler and colleagues3Sigler G. Anstee C. Seely A. Harmonization of adverse events monitoring following thoracic surgery: pursuit of a common language and methodology.J Thorac Cardiovasc Surg Open. 2021; 162: 250-256Google Scholar begin to tackle this challenge by presenting a standardized method for AE documentation consisting of a single set of drop-down menu options for classification of AEs. With their proposed modifications, the degree of harmonization with Canadian Association of Thoracic Surgeons increased with the ESTS (100%), STS (from 89% to 93%), Esophagectomy Complications Consensus Group Esophagectomy Complications Consensus Group (from 74% to 86%), and National Surgical Quality Improvement Program (from 73% to 91%) databases. The authors should be congratulated for their efforts to create a framework to unify the definitions of AEs among these data stakeholders. However, it should be recognized that the intent of these effort is not synonymous with linking databases. Currently, if an institution wishes to participate in multiple databases, registrars have to fill in AEs using separate systems, which is expensive and time-consuming. This particular limitation of AE reporting across systems is what the authors begin to address. In other words, the system proposed by Sigler and colleagues3Sigler G. Anstee C. Seely A. Harmonization of adverse events monitoring following thoracic surgery: pursuit of a common language and methodology.J Thorac Cardiovasc Surg Open. 2021; 162: 250-256Google Scholar simply makes it easier for a particular institution to simultaneously participate in AE reporting for multiple databases and potentially enables pooling of AE data. While this is a step in the right direction, limitations and challenges remain. Changes in the definitions of AEs over time create difficulties when performing longitudinal studies within a database. In addition, it should be noted that the authors’ proposal for harmonization is neither based on rigorous research nor the collective agreement of experts from multiple groups of stakeholders; it is based on the opinion of 2 authors. While a utopian world for thoracic surgery health services research would include the ability to link all large data sets with ease, there are practical and financial limitations. Finally, when attempting to compare data between datasets, one must recognize that each dataset has unique intents, strengths, and limitations and includes particular populations.1Groth S.S. Habermann E.B. Massarweh N.N. United States Administrative Databases and Cancer Registries for Thoracic Surgery Health Services Research.Ann Thorac Surg. 2020; 109: 636-644Abstract Full Text Full Text PDF PubMed Scopus (9) Google Scholar Nonetheless, we should recognize that we are an international community of cardiothoracic surgeons who should engage in more collaborative efforts, such as the STS-ESTS database, and strive to maximize the potential of these large datasets to optimize our outcomes and the quality of care we provide to our patients. Harmonization of adverse events monitoring following thoracic surgery: Pursuit of a common language and methodologyJTCVS OpenVol. 6PreviewThoracic surgery carries significant risk of postoperative adverse events (AEs). Multiple international recording systems are used to define and collect AEs following thoracic surgery procedures. We hypothesized that a simple-yet-ubiquitous approach to AE documentation could be developed to allow universal data entry into separate international databases. Full-Text PDF Open Access

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 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)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,188
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,001

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.

Tête enseignante Opus0,033
Tête enseignante GPT0,322
Écart entre enseignants0,289 · 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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

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