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Enregistrement W2973296614 · doi:10.1017/s0033291719001922

What to conclude from a non-randomized clinical trial comparing dialectical behavior therapy and mentalization-based treatment in patients with borderline personality disorder?

2019· letter· en· W2973296614 sur OpenAlexaff
Patrick Luyten, Falk Leichsenring, Allan Abbass, Mark J. Hilsenroth, Sven Rabung, Christiane Steinert

Notice bibliographique

RevuePsychological Medicine · 2019
Typeletter
Langueen
DomainePsychology
ThématiquePersonality Disorders and Psychopathology
Établissements canadiensDalhousie University
Organismes subventionnairesnon disponible
Mots-clésBorderline personality disorderContent (measure theory)MentalizationDialectical behavior therapyPsychologyPsychotherapistRandomized controlled trialAction (physics)Clinical psychologyMedicineInternal medicine

Résumé

récupéré en direct d'OpenAlex

The study by Barnicot and Crawford (Barnicot and Crawford, 2018) comparing clinical outcomes of Dialectical Behavior Therapy (DBT) and Mentalization-Based Treatment (MBT) in patients with Borderline Personality Disorder (BPD) in the context of a non-randomized study in the United Kingdom represents a major step forward in identifying effective treatments for BPD patients.Indeed, there is a lack of direct head-to-head comparisons of current evidencebased treatments of patients with BPD (Cristea et al., 2017;Fonagy et al., 2017).This study therefore provides important information concerning the relative effectiveness of both types of treatment, particularly because it was conducted in routine clinical care, increasing the ecological validity of its findings.Given the paucity of comparative studies, the use of appropriate analysis strategies and correct reporting of clinical trials in this area is all the more important.However, several problems with the data analysis and reporting make it unclear what conclusions can be drawn from this trial for both research and clinical practice.Barnicot and Crawford highlight in the abstract, results and discussion section of their paper that 'reductions in self-harm and improvements in emotional regulation at 12 months were greater amongst those receiving DBT than amongst those receiving MBT' (p.1), suggesting this was the major finding of their study.However, this conclusion seems not to be supported by the data.As noted by the authors themselves in the results section, their study found 'no differences between participants receiving DBT and those receiving MBT in number of incidents of selfharm, BPD severity, emotional dysregulation, relationships with others or dissociation' (p.4).Hence, no significant differences were found on any of the clinical outcome measures in this study.Still, in the abstract of their paper and in the discussion section the authors argue that reductions in self-harm and improvements in emotional regulation were greater in DBT.This erroneous conclusion appears to be based on the finding that in adjusted multilevel models there was a steeper decline in self-harm and emotional dysregulation in DBT compared to MBT.Yet, there is a clear difference between the rate of change during treatment and outcomes at the endpoint of a study.If there were no differences at the study endpoint, but there were differences in the rate of change, then patients simply followed different trajectories toward the same endpoint.If valid, these findings may have implications for clinical practice, even when DBT and MBT are equally effective at the study endpoint, as it would suggest that self-harm and associated features may improve faster in DBT.Furthermore, Barnicot and Crawford adjusted for baseline differences in several clinical variables between patients in MBT and DBT, of which some were significant, and others were not.Thus, the basis for their selection of potential confounding variables is not clear.Moreover, it is well-known that if covariates overlap with the experimental effect, adjusting for these variables does not balance out these differences, as is often wrongly assumed, but may instead obscure treatment effects if the covariates show a significant correlation with the outcome (Field, 2013).The authors did not report whether there was an association between any of these variables and outcomes.These considerations are relevant to the authors' analyses of several outcome measures.DBT showed significantly higher drop-out rates, hospitalization and emergency department attendance at 12-month follow-up.These differences disappeared after including covariates in the analyses.Whether this result is valid or not is not clear due to the problem of including covariates described above.

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)0,014
score de la tête « metaresearch » (Gemma)0,116
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Essai non randomisé · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,030
Score d'incertitude au seuil0,075

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0140,116
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0020,002
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,001
Communication savante0,0030,003
Science ouverte0,0020,001
Intégrité de la recherche0,0300,014
Charge utile insuffisante (le modèle a refusé de juger)0,0080,006

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 Opus0,108
Tête enseignante GPT0,427
Écart entre enseignants0,320 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeEssai non randomisé
Domainenon disponible
GenreCommentaire

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
Publié2019
Routes d'admission1
Résumé présentoui

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