Animal models of chemotherapy-induced peripheral neuropathy: A machine-assisted systematic review and meta-analysis.

We report a systematic review and meta-analysis of research using animal models of chemotherapy-induced peripheral neuropathy (CIPN). We systematically searched 5 online databases in September 2012 and updated the search in November 2015 using machine learning and text mining to reduce the screening for inclusion workload and improve accuracy. For each comparison, we calculated a standardised mean difference (SMD) effect size, and then combined effects in a random-effects meta-analysis. We assessed the impact of study design factors and reporting of measures to reduce risks of bias. We present power analyses for the most frequently reported behavioural tests; 337 publications were included. Most studies (84%) used male animals only. The most frequently reported outcome measure was evoked limb withdrawal in response to mechanical monofilaments. There was modest reporting of measures to reduce risks of bias. The number of animals required to obtain 80% power with a significance level of 0.05 varied substantially across behavioural tests. In this comprehensive summary of the use of animal models of CIPN, we have identified areas in which the value of preclinical CIPN studies might be increased. Using both sexes of animals in the modelling of CIPN, ensuring that outcome measures align with those most relevant in the clinic, and the animal's pain contextualised ethology will likely improve external validity. Measures to reduce risk of bias should be employed to increase the internal validity of studies. Different outcome measures have different statistical power, and this can refine our approaches in the modelling of CIPN.

Traumatic renal injury in a UK major trauma centre - current management strategies and the role of early re-imaging.

OBJECTIVES: To analyse the contemporary management of renal injuries in a UK major trauma centre and to evaluate the utility and value of re-imaging. PATIENTS AND METHODS: The prospectively maintained 'Trauma Audit and Research Network' database was interrogated to identify patients with urinary tract injuries between January 2014 and December 2017. Patients' records and imaging were reviewed to identify injury grades, interventions, outcomes, and follow-up. RESULTS: Renal injury was identified in 90 patients (79 males and 11 females). The mean (sd; range) age was 35.5 (17.4; 1.5-94) years. Most of the renal traumas were caused by blunt mechanisms (74%). The overall severity of injuries was: 18 (20%) Grade I, 19 (21%) Grade II, 27 (30%) Grade III, 22 (24%) Grade IV, and four (4%) Grade V. Most patients (84%) were managed conservatively. Early intervention (<24 h) was performed in 14 patients (16%) for renal injuries. Most of these patients were managed by interventional radiology techniques (nine of 14). Only two patients required an emergency nephrectomy, both of whom died from extensive polytrauma. In all, 19 patients underwent laparotomy for other injuries and did not require renal exploration. The overall 30-day mortality was 13%. Re-imaging was performed in 66% of patients at an average time of 3.4 days from initial scan. The majority of re-imaging was planned (49 patients) and 12% of these scans demonstrated a relevant finding (urinoma, pseudoaneurysm) that altered management in three of the 49 patients (6.1%). CONCLUSION: Non-operative management is the mainstay for all grades of injury. Haemodynamic instability and persistent urine leak are primary indications for intervention. Open surgical management is uncommon. Repeat imaging after injury is advocated for stable patients with high-grade renal injuries (Grade III-V), although more research is needed to determine the optimal timing.

Meta-analysis of the strength of exploratory suicide prediction models; from clinicians to computers.

BACKGROUND: Suicide prediction models have been formulated in a variety of ways and are heterogeneous in the strength of their predictions. Machine learning has been a proposed as a way of improving suicide predictions by incorporating more suicide risk factors. AIMS: To determine whether machine learning and the number of suicide risk factors included in suicide prediction models are associated with the strength of the resulting predictions. METHOD: Random-effect meta-analysis of exploratory suicide prediction models constructed by combining two or more suicide risk factors or using clinical judgement (Prospero Registration CRD42017059665). Studies were located by searching for papers indexed in PubMed before 15 August 2020 with the term suicid* in the title. RESULTS: In total, 86 papers reported 102 suicide prediction models and included 20 210 411 people and 106 902 suicides. The pooled odds ratio was 7.7 (95% CI 6.7-8.8) with high between-study heterogeneity (I2 = 99.5). Machine learning was associated with a non-significantly higher odds ratio of 11.6 (95% CI 6.0-22.3) and clinical judgement with a non-significantly lower odds ratio of 4.7 (95% CI 2.1-10.9). Models including a larger number of suicide risk factors had a higher odds ratio when machine-learning studies were included (P = 0.02). Among non-machine-learning studies, suicide prediction models including fewer risk factors performed just as well as those including more risk factors. CONCLUSIONS: Machine learning might have the potential to improve the performance of suicide prediction models by increasing the number of included suicide risk factors but its superiority over other methods is unproven.