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Record W2126855155 · doi:10.1503/cmaj.110490

Conversion disorder: advances in our understanding

2011· review· en· W2126855155 on OpenAlexaffvenue
Anthony Feinstein

Bibliographic record

VenueCanadian Medical Association Journal · 2011
Typereview
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineTofacitinibDeep veinPulmonary embolismRheumatoid arthritisMyocardial infarctionTocilizumabInternal medicineThrombosisStroke (engine)Emergency medicineIntensive care medicine

Abstract

fetched live from OpenAlex

<h3>Background:</h3> Inhibiting a specific JAK may impede more than one pathway, explaining both the efficacy and adverse effects observed with JAK inhibitors (JAKi). Among those, there have been recent concerns about potential thromboembolic risks with these drugs. As patients enrolled are not representative of all patients who may receive JAKi, data from trials are unlikely to provide definitive answers. Real impact of JAKi in real life on major cardiovascular events is not known. <h3>Objectives:</h3> To evaluate the risk of venous and arterial thromboembolic events with the use of JAKi in a real-world setting. <h3>Methods:</h3> A self-controlled case series analysis (method in which individuals act as their own control) was performed using data from the French national healthcare insurance system SNDS (“Système National des Données de Santé”), which included all anonymized individual level data about sociodemographic data, outpatient healthcare dispensed, hospital discharge summaries, and registration status for a list of 30 long term diseases. All patients treated by JAKi (baricitinib or tofacitinib) for rheumatoid arthritis, psoriasis arthritis and/or inflammatory bowel disease, and with at least one thromboembolic event (venous (VTE): deep vein thrombosis (DVT), pulmonary embolism (PE), arterial (ATE): acute coronary syndrome (ACS), myocardial infarction (MI), transient ischemic attack (TIA) and stroke) between 2017/11/01 and 2019/06/30 were included in the study. Associations were evaluated by incidence rate ratios (IRR), which compare the rate of events during exposed periods with rate of event during all other observed time periods. Exposed periods were defined as i) exposure to JAKi, ii) the month following exposure (post-exposure 1-30 days), and iii) long-term post-exposure (31 to 60 days). A pre-exposure period of 7 days was individualized to identify event-dependent probabilities of exposure and potential reverse causality bias, and all other periods were considered as non-exposed periods. <h3>Results:</h3> Among the 5,870 patients treated with JAKi between 2017/11/01 and 2019/06/30, 94 presented an incident thromboembolic event and were included. Almost two thirds were female (n=61, 64.9%), and median age was 65.4 [IQR: 55.5; 75.8] years. Most of patients have a rheumatoid arthritis (n=91, 96.8%), 62 (66.0%) were treated by baricitinib, and 32 (34.0%) by tofacitinib. Almost half (n=42, 44.7%) presented a venous thromboembolism, mainly DVT (n=31, 33.0%), and 52 (55.3%) presented an arterial thromboembolism, mainly MI (n=16, 17.0%) and stroke (n=14, 14.9%). Eleven patients (11.7%) died during the study period. The median time of occurrence of VTE was 4.3 [IQR: 2.5; 8.9] months, and 6.1 [IQR: 3.3; 9.2] months for ATE. The median duration of exposure was 6.0 [IQR: 3.3; 10.1] months for VTE, and 12.0 [IQR: 4.8; 15.3] months for ATE. The IRR for VTE and ATE were increased during exposure, and during the 30 days following exposure (Table 1). The IRR for VTE was only increased during exposure and in the early post-exposure phase contrary to the IRR for ATE that was also increased in the pre-exposure 7-Day period. Analysis conducted on survival patients confirmed results. <h3>Conclusion:</h3> The present study found an increased risk of VTE and ATE for baricitinib and tofacitinib. The risk persists in the month following the discontinuation of treatment but disappears after day 30 post-exposure. <h3>Disclosure of Interests:</h3> Amandine Gouverneur: None declared, Jérôme Avouac Consultant of: JA has/had consultancy relationship and/or has received research funding in the area of potential treatments for rheumatoid arthritis from (last three years): Abbvie, Galapagos, Pfizer, Bristol Myers Squibb, Sanofi, Nordic Pharma, Chugai and MSD., Grant/research support from: JA has/had consultancy relationship and/or has received research funding in the area of potential treatments for rheumatoid arthritis from (last three years): Abbvie, Galapagos, Pfizer, Bristol Myers Squibb, Sanofi, Nordic Pharma, Chugai and MSD., Clément Prati: None declared, Jean-Luc Cracowski: None declared, Thierry Schaeverbeke Consultant of: TS consultancy and/or research fundings: Abbvie, Lilly, Pfizer, Galapagos, Novartis, BMS, Medac, NordicPharma, Biogen, Mylan, Janssen., Grant/research support from: TS consultancy and/or research fundings: Abbvie, Lilly, Pfizer, Galapagos, Novartis, BMS, Medac, NordicPharma, Biogen, Mylan, Janssen., Antoine Pariente Grant/research support from: AP reports acting as an independant expert towards the French Medicines Agency (Agence Nationale de Securité du Médicament et des Produits de Santé, ANSM) and the European Medicines Agency (EMA). AP coordinates the DRUGS Systematised Assessment in real-liFe EnviRonment (DRUGS-SAFER) programme funded by the Agence Nationale de Sécurité du Médicament et des Produits de Santé (ANSM)., Marie-Elise Truchetet Consultant of: has/had consultancy relationship and/or has received research funding in the area of potential treatments for rheumatoid arthritis and spondyloarthritis and their complications from (last three years): Abbvie, Galapagos, Lilly, Medac, Novartis, Pfizer, and Roche., Grant/research support from: has/had consultancy relationship and/or has received research funding in the area of potential treatments for rheumatoid arthritis and spondyloarthritis and their complications from (last three years): Abbvie, Galapagos, Lilly, Medac, Novartis, Pfizer, and Roche.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.973
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.035
GPT teacher head0.317
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations86
Published2011
Admission routes2
Has abstractyes

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