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Cancer-related fatigue is one of the most prevalent and distressing symptoms among cancer patients, resulting in a great cancer research challenge. Numerous systematic reviews of physical training interventions have been conducted to find the most effective approach. However, evidence remains fragmented, and in which cancer population physical training is more effective than other populations is still unclear. Thus, this study critically appraised systematic reviews and meta-analyses on physical training to reduce adults' cancer-related fatigue. A systematic review of systematic reviews and meta-analysis (PROSPERO: CRD42020189049), assessing the efficacy of exercise training for reducing cancer-related fatigue in adults, was conducted in PubMed, CINAHL, Cochrane Database of Systematic Reviews, and the Database of Abstracts of Reviews of Effects, and Pedro. The selected studies (standardized mean difference, SMD; 95%CI), was quantitatively pooled using a random-effects model. Heterogeneity was tested using chi-squared (Q) and I-square statistics (I2). Of 1438 identified articles, 11 met the inclusion criteria, and ten were meta-analyzed. The results yielded a positive effect of physical training on fatigue in all cancer populations, SMD = −0.33 (−0.43, −0.23). Subgroup analysis based on tumor localization showed a slightly higher physical training effect on fatigue in adults with breast cancer, SMD = −0.36 (−0.57, −0.15), and prostate cancer SMD = −0.34 (−0.45, −.0.22). Our analysis demonstrated some potential improvement in cancer-related fatigue in adult patients undergoing physical training during and after cancer treatments, particularly in patients with breast or prostate cancer. [ABSTRACT FROM AUTHOR]

Short-course radiotherapy with consolidation chemotherapy (SCRT-CCT) has emerged as a promising alternative to the long course chemoradiotherapy (LCRT) regimen in locally advanced rectal cancer management. The systematic review and meta-analysis is aimed at summarizing current evidence on SCRT-CCT and comparing it to LCRT. Electronic databases of MEDLINE, Web of Science, and Cochrane library were searched using a predefined search strategy returning 3314 articles. This review included 11 studies (6 randomized trials and 5 non-randomized studies) on SCRT-CCT regimen based on seven different cohorts. Weighted arithmetic means and forest plots were generated to determine summary estimates. The probability of achieving pathological complete response (pCR) was higher with SCRT-CCT compared to LCRT (risk ratio [RR] = 1.75, 95% confidence interval [CI]: 1.41–2.19). No statistically significant difference in 3-year overall survival (OS) was observed between the two groups (RR= 1.06, 95% CI: 0.98–1.14). The weighted arithmetic mean of 3-year OS and pCR was 83.6% versus 80.9%, and 24.5% versus 13.6% for SCRT-CCT and LCRT, respectively. R0 resection and T-downstaging rates ranged from 69.2–100% to 47–75% for SCRT-CCT, and 71–92.3% and 41–75% for LCRT, respectively. The regimens had similar compliance, postoperative, and late toxicity, however, acute toxicity rates varied primarily due to differences in treatment protocols. This review highlights the ability of SCRT-CCT to produce improved tumor response with comparable OS, R0 resection, and T-downstaging at the cost of increased acute toxicity. However, heterogeneity in treatment protocols across studies makes it difficult to provide definitive conclusions regarding the regimen. Several ongoing trials are expected to provide further evidence confirming the findings of RAPIDO trial and detail appropriate SCRT-CCT protocols to improve oncological outcomes, minimize toxicity, and determine its effectiveness as the standard-of-care for locally advanced rectal cancer patients. [ABSTRACT FROM AUTHOR]

Background and purpose — External validation of machine learning (ML) prediction models is an essential step before clinical application. We assessed the proportion, performance, and transparent reporting of externally validated ML prediction models in orthopedic surgery, using the Transparent Reporting for Individual Prognosis or Diagnosis (TRIPOD) guidelines. Material and methods — We performed a systematic search using synonyms for every orthopedic specialty, ML, and external validation. The proportion was determined by using 59 ML prediction models with only internal validation in orthopedic surgical outcome published up until June 18, 2020, previously identified by our group. Model performance was evaluated using discrimination, calibration, and decision-curve analysis. The TRIPOD guidelines assessed transparent reporting. Results — We included 18 studies externally validating 10 different ML prediction models of the 59 available ML models after screening 4,682 studies. All external validations identified in this review retained good discrimination. Other key performance measures were provided in only 3 studies, rendering overall performance evaluation difficult. The overall median TRIPOD completeness was 61% (IQR 43–89), with 6 items being reported in less than 4/18 of the studies. Interpretation — Most current predictive ML models are not externally validated. The 18 available external validation studies were characterized by incomplete reporting of performance measures, limiting a transparent examination of model performance. Further prospective studies are needed to validate or refute the myriad of predictive ML models in orthopedics while adhering to existing guidelines. This ensures clinicians can take full advantage of validated and clinically implementable ML decision tools. [ABSTRACT FROM AUTHOR]