The Meta-Analysis of Clinical Judgment Project: Fifty-Six Years of Accumulated Research on Clinical Versus Statistical Prediction

Background: C3 glomerulopathy (C3G), a rare glomerular disease mediated by alternative complement pathway dysregulation, is associated with a high rate of recurrence and graft loss after kidney transplantation (KTx). We aimed to assess the efficacy of different treatments for C3G recurrence after KTx. Methods: Databases (MEDLINE, EMBASE, and Cochrane Database) were searched from inception through 3 May, 2019. Studies were included that reported outcomes of adult KTx recipients with C3G. Effect estimates from individual studies were combined using the random-effects, generic inverse variance method of DerSimonian and Laird., The protocol for this meta-analysis is registered with PROSPERO (no. CRD42019125718). Results: Twelve studies (7 cohort studies and 5 case series) consisting of 122 KTx patients with C3G (73 C3 glomerulonephritis (C3GN) and 49 dense deposit disease (DDD)) were included. The pooled estimated rates of allograft loss among KTx patients with C3G were 33% (95% CI: 12–57%) after eculizumab, 42% (95% CI: 2–89%) after therapeutic plasma exchange (TPE), and 81% (95% CI: 50–100%) after rituximab. Subgroup analysis based on type of C3G was performed. Pooled estimated rates of allograft loss in C3GN KTx patients were 22% (95% CI: 5–46%) after eculizumab, 56% (95% CI: 6–100%) after TPE, and 70% (95% CI: 24–100%) after rituximab. Pooled estimated rates of allograft loss in DDD KTx patients were 53% (95% CI: 0–100%) after eculizumab. Data on allograft loss in DDD after TPE (1 case series, 0/2 (0%) allograft loss at 6 months) and rituximab (1 cohort, 3/3 (100%) allograft loss) were limited. Among 66 patients (38 C3GN, 28 DDD) who received no treatment (due to stable allograft function at presentation and/or clinical judgment of physicians), pooled estimated rates of allograft loss were 32% (95% CI: 7–64%) and 53% (95% CI: 28–77%) for C3GN and DDD, respectively. Among treated C3G patients, data on soluble membrane attack complex of complement (sMAC) were limited to patients treated with eculizumab (N = 7). 80% of patients with elevated sMAC before eculizumab responded to treatment. In addition, all patients who responded to eculizumab had normal sMAC levels after post-eculizumab. Conclusions: Our study suggests that the lowest incidence of allograft loss (33%) among KTX patients with C3G are those treated with eculizumab. Among those who received no treatment for C3G due to stable allograft function, there is a high incidence of allograft loss of 32% in C3GN and 53% in DDD. sMAC level may help to select good responders to eculizumab.

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Syncope Risk Stratification Tools vs Clinical Judgment: An Individual Patient Data Meta-analysis

The American Journal of Medicine ◽

10.1016/j.amjmed.2014.05.022 ◽

2014 ◽

Vol 127 (11) ◽

pp. 1126.e13-1126.e25 ◽

Cited By ~ 54

Author(s):

Giorgio Costantino ◽

Giovanni Casazza ◽

Matthew Reed ◽

Ilaria Bossi ◽

Benjamin Sun ◽

...

Keyword(s):

Risk Stratification ◽

Clinical Judgment ◽

Individual Patient Data ◽

Meta Analysis ◽

Patient Data

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Theories of clinical judgment and decision-making: A review of the theoretical literature

Australasian Journal of Paramedicine ◽

10.33151/ajp.3.1.308 ◽

2015 ◽

Vol 3 (1) ◽

Cited By ~ 2

Author(s):

Ramon Shaban

Keyword(s):

Decision Making ◽

Clinical Judgment ◽

Meta Analysis ◽

Judgment And Decision Making ◽

Human Judgment ◽

Theoretical Literature ◽

Insight Into ◽

Do So

This paper provides a survey of the terrain of theories of human judgment and decision-making (JDM). It provides an introduction, overview, and some insight into the understanding of some conceptual theories, frameworks, and the literature of JDM. This paper is in no way an exhaustive meta-analysis of the literature on JDM, nor is it intended to be. It does not seek to categorise and compare existing theories of judgment and decision-making or critically evaluate each in terms of others, nor does it seek to reclassify existing categories. Indeed much of the debate in the literature is about that very issue—how researchers and theorists view, characterise, categorise and apply existing theory of JDM in existing philosophies, ‘schools-of-thought’, and professional domains. The problematic, controversial, and, in the view of some researchers, inappropriate attempts to do so are well-documented [1-4]. This paper will provide an overview of the competing accounts that various theories and philosophies place on judgment and decision-making.

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Artificial Intelligence and Machine Learning Algorithms For Informing the Diagnostic Process of Mild Cognitive Impairment and Dementia

Archives of Clinical Neuropsychology ◽

10.1093/arclin/acz035.06 ◽

2019 ◽

Vol 34 (6) ◽

pp. 838-838

Author(s):

J Gardner

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Sensitivity And Specificity ◽

Clinical Judgment ◽

Meta Analysis ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Bivariate Correlation

Abstract Objective Differentiating between a clinical diagnosis of Mild Cognitive Impairment (MCI) and dementia is difficult due to expansive data needs in concert with ambiguity of clinical criteria. Novel artificial intelligence (AI) and machine learning algorithms provide potential avenues for efficiently analyzing data sets and informing clinical judgment in distinguishing MCI from dementia. To date no formal meta-analysis of extant studies has been conducted to compare the efficacy of such procedures. A meta-analysis was conducted to synthesize the sensitivity and specificity of AI and machine learning programs in distinguishing between MCI and dementia as compared to traditional diagnostic protocols. Data Selection A search of studies using EBSCOhost databases using the keywords: “artificial intelligence,” “machine learning,” “MCI,” and “dementia” retrieved a total of 127 studies. Excluded were 106 studies due to non-reporting of sensitivity and specificity data. In total, 21 studies were included in the present meta-analysis. Data Synthesis Sensitivity and specificity data as well as the number of true-false categorizations were extracted and analyzed using OpenMeta[Analyst]. A bivariate correlation produced a summary point with sensitivity of 82% and specificity of 82%. A follow-up Rutter-Gatsonis multivariate correlation HSROC curve was created to correct for significant correlations (47%), and produced an adjusted mean specificity of 79% and sensitivity of 83%. Conclusions Results suggest AI and machine-learning algorithms are effective in distinguishing MCI from dementia. AI procedures have potential in aiding clinical judgment given a larger body of empirical research.

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Risk Factors for Psychotic Relapse After Dose Reduction or Discontinuation of Antipsychotics in Patients With Chronic Schizophrenia: A Systematic Review and Meta-analysis

Schizophrenia Bulletin Open ◽

10.1093/schizbullopen/sgaa002 ◽

2020 ◽

Vol 1 (1) ◽

Cited By ~ 1

Author(s):

Jan P A M Bogers ◽

George Hambarian ◽

Maykel Michiels ◽

Jentien Vermeulen ◽

Lieuwe de Haan

Keyword(s):

Risk Factors ◽

Dose Reduction ◽

Clinical Judgment ◽

Rating Scales ◽

Meta Analysis ◽

Chronic Schizophrenia ◽

Patient Characteristics ◽

Study Characteristics ◽

Potential Risk Factors ◽

High Doses

Abstract High doses of antipsychotics in patients with chronic schizophrenia might lead to more severe side effects and possibly hamper recovery, but dose reduction carries the risk of psychotic relapse. It would be helpful to establish risk factors for relapse during dose reduction. We systematically searched MEDLINE, EMBASE, and PsycINFO from January 1950 through June 2019 and reviewed studies that reported on relapse rates (event rates [ERs]) after dose reduction or discontinuation of antipsychotics in cohorts of patients with chronic schizophrenia. We calculated ERs (with 95% CIs) per person-year and sought to identify potential risk factors, such as patient characteristics, dose reduction/discontinuation characteristics, and study characteristics. Of 165 publications, 40 describing dose reduction or discontinuation in 46 cohorts (1677 patients) were included. The pooled ER for psychotic relapse was 0.55 (95% CI 0.46–0.65) per person-year. The ER was significantly higher in inpatients, patients with a shorter duration of illness, patients in whom antipsychotics were discontinued or in whom the dose was reduced to less than 5 mg haloperidol equivalent, studies with a short follow-up or published before 1990, and studies in which relapse was based on clinical judgment (ie, rating scales were not used). Clinicians should consider several robust risk factors for psychotic relapse in case of dose reduction in chronic schizophrenia.

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Another Perspective on Clinical Judgment

Psychological Reports ◽

10.2466/pr0.1975.36.2.383 ◽

1975 ◽

Vol 36 (2) ◽

pp. 383-389 ◽

Cited By ~ 2

Author(s):

Martin M. Shinedling ◽

Robert J. Howell ◽

Gary Carlson

Keyword(s):

Decision Making ◽

Doctoral Students ◽

Clinical Judgment ◽

Clinical Decision Making ◽

Clinical Decision ◽

Statistical Prediction ◽

Theoretical Limit ◽

Statistical Decision ◽

Statistical Decision Making ◽

Predicting Behavior

This study transformed the clinical versus actuarial controversy into an analysis of rule-of-thumb versus statistical decision-making strategies. To make direct comparisons, clinical and actuarial decision-making strategies by 10 doctoral students in psychology were translated into computer programs and their accuracy was evaluated in terms of classification criteria. Resulting analyses indicated that programmed clinical decision-making strategies, ‘clinistics,’ can contribute to the prediction of behavior. Such a contribution may be of great value inasmuch as statistical prediction may already have reached its theoretical limit in predicting behavior.

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