OBJECTIVE To review and critically appraise published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of becoming infected with covid-19 or being admitted to hospital with the disease. DESIGNLiving systematic review and critical appraisal. DATA SOURCESPubMed and Embase through Ovid, Arxiv, medRxiv, and bioRxiv up to 7 April 2020.Cite this as: BMJ 2020;369:m1328 http://dx.

Background: Clinical prediction models combine several predictors (risk or prognostic factors) to estimate the risk whether a particular condition is present (diagnostic model) or whether a certain event will occur in the future (prognostic model). Large numbers of diagnostic and prognostic prediction model studies are published each year and a tool facilitating their quality assessment is needed, e.g. to support systematic reviews and evidence syntheses.Objective: To introduce and describe the development of PROBAST, a tool for assessing the risk of bias and applicability of prediction model studies.Methods: Web-based Delphi procedure (involving 40 experts in the field of prediction model research) and refinement of the tool through piloting. The scope of PROBAST was determined with consideration of existing risk of bias tools and reporting guidelines, such as CHARMS, QUADAS, QUIPS, and TRIPOD.Results: After seven Delphi rounds, a final tool was developed which utilises a domain-based structure supported by signalling questions. PROBAST assesses the risk of bias of prediction model studies and any concerns for their applicability. Studies that PROBAST can be used for include those developing, validating, and extending a prediction model. We define risk of bias to occur when shortcomings in the study design, conduct or analysis lead to systematically distorted estimates of model predictive performance or to an inadequate model to address the research question. The predictive performance is typically evaluated using calibration and discrimination, and sometimes (notably in diagnostic model studies) classification measures. Applicability refers to the extent to which the prediction model study matches the systematic review question in terms of the target population, predictors, or outcomes of interest. PROBAST comprises 20 signalling questions grouped into four domains: participant selection, predictors, outcome, and analysis.Conclusions: PROBAST can be used to assess the risk of bias and any concerns for applicability of studies developing, validating or extending (adjusting) prediction, both diagnostic and prognostic, models.

Types of Predictors, Outcomes, and Modeling TechniquesPROBAST can be used to assess any type of diagnostic or prognostic prediction model aimed at individualized predictions regardless of the predictors used; outcomes being predicted; or methods used to develop, validate, or update (for example, extend) the model.Predictors range from demographic characteristics, medical history, and physical examination results; to imaging results, electrophysiology, blood, urine, or tissue measurements, and disease stages or characteristics; to results from "omics" and other new biological measurements. Predictors are also called covariates, risk indicators, prognostic factors, determinants, index test results, or independent variables (4, 6 -8, 49, 50, 55, 56, 57).PROBAST distinguishes between candidate predic-Prediction model external validation: These studies aim to assess the predictive performance of existing prediction models using data external to the development sample (i.e., from different participants).Adopted from the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) and CHARMS (CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies) guidance (8, 16).

Importance Cannabis and cannabinoid drugs are widely used to treat disease or alleviate symptoms, but their efficacy for specific indications is not clear.Objective To conduct a systematic review of the benefits and adverse events (AEs) of cannabinoids.Data Sources Twenty-eight databases from inception to April 2015. Study SelectionRandomized clinical trials of cannabinoids for the following indications: nausea and vomiting due to chemotherapy, appetite stimulation in HIV/AIDS, chronic pain, spasticity due to multiple sclerosis or paraplegia, depression, anxiety disorder, sleep disorder, psychosis, glaucoma, or Tourette syndrome. Data Extraction and SynthesisStudy quality was assessed using the Cochrane risk of bias tool. All review stages were conducted independently by 2 reviewers. Where possible, data were pooled using random-effects meta-analysis. Main Outcomes and MeasuresPatient-relevant/disease-specific outcomes, activities of daily living, quality of life, global impression of change, and AEs.Results A total of 79 trials (6462 participants) were included; 4 were judged at low risk of bias. Most trials showed improvement in symptoms associated with cannabinoids but these associations did not reach statistical significance in all trials. Compared with placebo, cannabinoids were associated with a greater average number of patients showing a complete nausea and vomiting response (47% vs 20%; odds ratio [OR], 3.82 [95% CI, 1.55-9.42]; 3 trials), reduction in pain (37% vs 31%; OR, 1.41 [95% CI, 0.99-2.00]; 8 trials), a greater average reduction in numerical rating scale pain assessment (on a 0-10-point scale; weighted mean difference [WMD], −0.46 [95% CI, −0.80 to −0.11]; 6 trials), and average reduction in the Ashworth spasticity scale (WMD, −0.12 [95% CI, −0.24 to 0.01]; 5 trials). There was an increased risk of short-term AEs with cannabinoids, including serious AEs. Common AEs included dizziness, dry mouth, nausea, fatigue, somnolence, euphoria, vomiting, disorientation, drowsiness, confusion, loss of balance, and hallucination. Conclusions and RelevanceThere was moderate-quality evidence to support the use of cannabinoids for the treatment of chronic pain and spasticity. There was low-quality evidence suggesting that cannabinoids were associated with improvements in nausea and vomiting due to chemotherapy, weight gain in HIV infection, sleep disorders, and Tourette syndrome. Cannabinoids were associated with an increased risk of short-term AEs.

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