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Enregistrement W2928924896 · doi:10.1136/bmjebm-2018-111070.82

82 Defining overdiagnosis of mental health disorders: secondary analysis of an overdiagnosis scoping review

2018· article· en· W2928924896 sur OpenAlexaff
Kimberly A. Turner, Ian Shrier, Brett D. Thombs

Notice bibliographique

RevueOral Presentations · 2018
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueHealth Systems, Economic Evaluations, Quality of Life
Établissements canadiensJewish General HospitalMcGill University
Organismes subventionnairesnon disponible
Mots-clésOverdiagnosisPsychological interventionOperationalizationMental healthNeurocognitiveMedicineAsymptomaticPsychologyPsychiatryCognitionPathologyEpistemology

Résumé

récupéré en direct d'OpenAlex

Objectives The term ‘overdiagnosis’ was first used as early as 1924, when J. D. Adamson used it to describe negative implications of attempts to achieve early diagnosis of pulmonary tuberculosis (CMAJ, 1924). Since then, the term has been used most frequently to describe negative outcomes from screening for early-stage asymptomatic cancers. Typical definitions describe overdiagnosis as detection of early-stage asymptomatic conditions that would never have led to morbidity or mortality. In other areas, including mental health, overdiagnosis can occur among people who experience symptoms but whose symptoms do not reflect disorders and may not be amenable to healthcare interventions. Thus, more recent alternative definitions have focused on diagnosis among people who would not be expected to experience net benefit. The degree to which such definitions have been adopted is not clear. Our objective was to describe how the term overdiagnosis has been defined explicitly or operationalized implicitly in mental health. Method A scoping review of overdiagnosis across medical disciplines searched PubMed in August 2017 for published articles that used keywords related to overdiagnosis. Articles from the scoping review were eligible for the present analysis if they were classified in the scoping review as related to mental health, excluding neurocognitive disorders, and if they used the term overdiagnosis in the text of the article and not just in the title. We extracted basic information about the article and whether it included an explicit or implicit definition of overdiagnosis. Explicit definitions were extracted. If the definition was implicit, the reviewer provided an explanation of how overdiagnosis was operationalized in the study or article. Data were extracted by one reviewer with validation by a second reviewer, and any disagreements resolved by consensus. Explicit and implicit definitions were grouped into categories by one investigator and verified by a second investigator. Results 148 articles were included. Of the 14 articles that explicitly defined overdiagnosis, 9 defined it as a false positive diagnosis, 2 as misdiagnosis (diagnosing people with one disorder rather than another), 1 as diagnosis of an individual who would not be expected to benefit from treatment, and 2 had vague descriptions. In the other 134 articles, implicit definitions fit into 4 categories; 68 articles implicitly defined overdiagnosis as diagnosis of people who do not meet diagnostic criteria, 59 as misdiagnosis, 13 as diagnosis resulting from overly broad or changed diagnostic criteria; and 2 as no net benefit from diagnosis. There were 13 with unclear or difficult to classify definitions. There was overlap of definitions with several articles fitting into more than one category. The most significant overlap involved 13 articles that were classified as both misdiagnosis and diagnosis of people who do not meet diagnostic criteria. Conclusions Definitions of overdiagnosis commonly used in the context of screening for asymptomatic early-stage disease are not generally applicable in mental health where diagnoses are not made in the absence of symptoms. There is not, however, an agreed upon definition of overdiagnosis in mental health. Results from the present review indicate that the term is used most commonly in the field to describe potential drivers of overdiagnosis, including diagnosing individuals who do not meet diagnostic criteria and overly broad diagnostic criteria, as well as misdiagnosis, which may not always reflect overdiagnosis. Some articles define overdiagnosis in mental health as occurring when there is no net benefit from diagnosis; that is, when individuals are diagnosed, but expected benefits from the diagnosis would not be expected to exceed harms. Agreement on an approach to defining overdiagnosis in mental health is needed so that evidence of overdiagnosis can be more readily evaluated.

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)0,112
score de la tête « metaresearch » (Gemma)0,334
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: Méthodes · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: Revue systématique
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,888
Score d'incertitude au seuil0,593

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,1120,334
Méta-épidémiologie (sens strict)0,0020,002
Méta-épidémiologie (sens large)0,0080,016
Bibliométrie0,0510,048
Études des sciences et des technologies0,0020,002
Communication savante0,0060,004
Science ouverte0,0030,007
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0090,001

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 Opus0,285
Tête enseignante GPT0,508
Écart entre enseignants0,222 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeRevue systématique
DomaineMéthodes
GenreEmpirique

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
Publié2018
Routes d'admission1
Résumé présentoui

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