The number of papers presenting machine learning (ML) models that are being submitted to and published in the Journal of Medical Internet Research and other JMIR Publications journals has steadily increased. Editors and peer reviewers involved in the review process for such manuscripts often go through multiple review cycles to enhance the quality and completeness of reporting. The use of reporting guidelines or checklists can help ensure consistency in the quality of submitted (and published) scientific manuscripts and, for example, avoid instances of missing information. In this Editorial, the editors of JMIR Publications journals discuss the general JMIR Publications policy regarding authors\' application of reporting guidelines and specifically focus on the reporting of ML studies in JMIR Publications journals, using the Consolidated Reporting of Machine Learning Studies (CREMLS) guidelines, with an example of how authors and other journals could use the CREMLS checklist to ensure transparency and rigor in reporting.

To evaluate the diagnostic value of serum/urinary biomarkers and the operability diagnosis strategy to make management recommendations.
Bibliographical search in French and English languages by consultation of Pubmed, Cochrane and Embase databases.
For the diagnosis of a suspicious adnexal mass on imaging: Serum CA125 antigen is recommended (grade A). Serum CAE is not recommended (grade C). The low evidence in literature concerning diagnostic value of CA19.9 does not allow any recommendation concerning its use. Serum Human epididymis protein 4 (HE4) is recommended (grade A). Comparison of data concerning diagnosis value of CA125 and HE4 show similar results for the prediction of malignancy in case of a suspicious adnexal mass on imaging (NP1). Urinary HE4 is not recommended (grade A). The use of circulating tumor DNA is not recommended (grade A). Tumor associated antigen-antibodies (AAbs) is not recommended (grade B). The use of ROMA score (Risk of Ovarian Malignancy Algorithm) is recommended (grade A). The use of Copenhagen index (CPH-I), R-OPS score, OVA500 is not recommended (grade C). For the prediction of resectability of an ovarian cancer with peritoneal carcinomatosis in the context of a primary debulking surgery: It is not recommendend to use serum CA125 (grade A). The low evidence in literature concerning diagnostic value of HE4 does not allow any recommendation concerning its use in this context. No recommendation can be given concerning CA19.9 and CAE. For the prediction of resectability of an ovarian cancer with peritoneal carcinomatosis in the context of surgery after neoadjuvant chemotherapy: the low evidence in literature concerning diagnostic value of serum markers in this context does not allow any recommendation concerning their use in this context. Place of laparoscopy for the prediction of resectability in case of upfront surgery of an ovarian cancer with peritoneal carcinomatosis robust data shows that the use of laparoscopy significantly reduce futile laparotomies (LE1). Laparoscopy is recommended in this context (grade A). Fagotti score is a reproducible tool (LE1) permitting the evaluation of feasibility of an optimal upfront debulking (NP4), its use is recommended (grade C). A Fagotti score≥8 is correlated to a low probability of complete or optimal debulking surgery (LE4) (grade C). There is no sufficient evidence to recommend the use of the modified Fagotti score or any other laparoscopic score (LE4). In case of laparotomy for an ovarian cancer with peritoneal carcinomatosis, the use of Peritoneal Cancer Index (PCI) is recommended (grade C). For the prediction of overall survival, disease free survival and the prediction of postoperative complications, the clinical and statistical of actually available tools do not allow any recommendation.

Eight software applications are compared for their performance in estimating the octanol-water partition coefficient (Kow), melting point, vapor pressure and water solubility for a dataset of polychlorinated biphenyls, polybrominated diphenyl ethers, polychlorinated dibenzodioxins, and polycyclic aromatic hydrocarbons. The predicted property values are compared against a curated dataset of measured property values compiled from the scientific literature with careful consideration given to the analytical methods used for property measurements of these hydrophobic chemicals. The variability in the predicted values from different calculators generally increases for higher values of Kow and melting point and for lower values of water solubility and vapor pressure. For each property, no individual calculator outperforms the others for all four of the chemical classes included in the analysis. Because calculator performance varies based on chemical class and property value, the geometric mean and the median of the calculated values from multiple calculators that use different estimation algorithms are recommended as more reliable estimates of the property value than the value from any single calculator.

BACKGROUND: The recent explosion in available electronic health record (EHR) data is motivating a rapid expansion of electronic health care predictive analytic (e-HPA) applications, defined as the use of electronic algorithms that forecast clinical events in real time with the intent to improve patient outcomes and reduce costs. There is an urgent need for a systematic framework to guide the development and application of e-HPA to ensure that the field develops in a scientifically sound, ethical, and efficient manner.
OBJECTIVE: Building upon earlier frameworks of model development and utilization, we identify the emerging opportunities and challenges of e-HPA, propose a framework that enables us to realize these opportunities, address these challenges, and motivate e-HPA stakeholders to both adopt and continuously refine the framework as the applications of e-HPA emerge.
METHODS: To achieve these objectives, 17 experts with diverse expertise including methodology, ethics, legal, regulation, and health care delivery systems were assembled to identify emerging opportunities and challenges of e-HPA and to propose a framework to guide the development and application of e-HPA.
RESULTS: The framework proposed by the panel includes three key domains where e-HPA differs qualitatively from earlier generations of models and algorithms (Data Barriers, Transparency, and ETHICS) and areas where current frameworks are insufficient to address the emerging opportunities and challenges of e-HPA (Regulation and Certification; and Education and Training). The following list of recommendations summarizes the key points of the framework: Data Barriers: Establish mechanisms within the scientific community to support data sharing for predictive model development and testing.Transparency: Set standards around e-HPA validation based on principles of scientific transparency and reproducibility.
METHODS: Develop both individual-centered and society-centered risk-benefit approaches to evaluate e-HPA.Regulation and Certification: Construct a self-regulation and certification framework within e-HPA.Education and Training: Make significant changes to medical, nursing, and paraprofessional curricula by including training for understanding, evaluating, and utilizing predictive models.