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Enregistrement W2600272500

Developing a standardized tool for interpretation of radiology diagnostic accuracy trials

2016· dissertation· en· W2600272500 sur OpenAlexaboutno aff
Betty Anne Schwarz

Notice bibliographique

RevueMiddlesex University Research Repository (Middlesex University Of London) · 2016
Typedissertation
Langueen
DomaineSocial Sciences
ThématiqueDelphi Technique in Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésDiagnostic accuracyMedical physicsMedicineGold standard (test)Radiological weaponRadiology
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Summary \nWithin the health sciences, action research is a methodology well suited to the goal of collaboratively improving practice. As the Royal College of Radiology recommends the use of published clinical trials as guides for achieving higher standards of accuracy, it is important for radiologists to reflect deeply on the results from diagnostic accuracy studies. When the results of the gold standard (or reference standard) are used to confirm a particular diagnosis or disease by comparing the diagnostic accuracy to a newer or index test, this is referred to as diagnostic accuracy research. In the reporting of all research, every effort must be made to reduce the incidence of bias. In 2003, the STARD (Standards for Reporting Diagnostic Accuracy) tool was developed for clinicians to enhance the quality of reporting diagnostic accuracy studies. Based on previous studies, experiential knowledge, and an extensive review of the literature, this research demonstrates that the STARD tool is not being fully optimized. The overall aim of this research was to conduct a work-based project within the department of radiology to develop a revised tool, based on the current STARD, which could then be used to more accurately report and interpret the results of radiology diagnostic accuracy trials. This study was conducted in accordance with participatory action research. \n \nMethods \nThe development of this new reporting tool was conducted in collaboration with a group of physicians, and in two distinct phases. First, a needs assessment was sent to eight radiological experts who had agreed to participate in the study. Based on their responses, and feedback from my mentor and colleagues, the next phase of tool development was done using the Delphi technique, after two rounds of which consensus was met. Each phase and cycle iteration to complete the needs assessment and Delphi technique are synonymous with the cycles of action research. The new reporting tool was named the RadSTARD (Radiology Standards for the Reporting of Diagnostic Accuracy Studies), and an elaboration document was written to provide guidance to the end-user. Radiology residents and Fellows at The Ottawa Hospital were then asked to rate their level of confidence in interpreting a diagnostic accuracy article specific to radiology while referring to the RadSTARD. They were also provided a second diagnostic article, the STARD tool, and an elaboration document for comparison. Data was collected using questionnaires that allowed for additional comments. \n \nFindings \nThe validation phase of the RadSTARD tool was completed via triangulation of data, as both a quantitative and qualitative analysis was completed. The results found no significant statistical difference between the two groups as per the Mann-Whitney and chi-square analysis. Likewise, both physician groups indicated that they found RadSTARD increased their level of confidence when interpreting the diagnostic accuracy article. Concomitantly, when combined, 96% of the two physician groups indicated they would use the tool again. \n \nInterpretation \nThese results may be interpreted as generalizable, as there was no discrepancy or statistical difference found in the results between the radiology residents’ and Fellows’ scores, despite the differences in their level of training. Both groups found the RadSTARD tool and elaboration document to be beneficial to them when interpreting the literature. RadSTARD is thus a reliable tool that can be used to validate the results of diagnostic accuracy studies specific to radiology. It will aid radiologists in reporting and interpreting radiology diagnostic accuracy studies, impacting their practice for generations to come.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,550
score de la tête « metaresearch » (Gemma)0,657
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesMétarecherche
DomaineSignal candidat: Méthodes · Signal consensuel: Méthodes
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Méthodes · Signal consensuel: Méthodes
Score de désaccord entre enseignants0,450
Score d'incertitude au seuil0,555

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,5500,657
Méta-épidémiologie (sens strict)0,0030,003
Méta-épidémiologie (sens large)0,0040,006
Bibliométrie0,0270,016
Études des sciences et des technologies0,0030,005
Communication savante0,0130,013
Science ouverte0,0080,010
Intégrité de la recherche0,0030,009
Charge utile insuffisante (le modèle a refusé de juger)0,0130,007

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 Opus0,142
Tête enseignante GPT0,427
Écart entre enseignants0,285 · 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; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.

Devis d'étudeQualitatif
DomaineMéthodes
GenreMéthodes

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
Publié2016
Routes d'admission1
Résumé présentoui

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