Data solidarity for machine learning for embryo selection: a call for the creation of an open access repository of embryo data.

The last decade has seen an explosion of machine learning applications in healthcare, with mixed and sometimes harmful results despite much promise and associated hype. A significant reason for the reversal in the reported benefit of these applications is the premature implementation of machine learning algorithms in clinical practice. This paper argues the critical need for 'data solidarity' for machine learning for embryo selection. A recent Lancet and Financial Times commission defined data solidarity as 'an approach to the collection, use, and sharing of health data and data for health that safeguards individual human rights while building a culture of data justice and equity, and ensuring that the value of data is harnessed for public good' (Kickbusch et al., 2021).

Ability of triage nurses to predict, at the time of triage, the eventual disposition of patients attending the emergency department (ED): a systematic literature review and meta-analysis.

INTRODUCTION: Exit block is the most significant cause of poor patient flow and crowding in the emergency department (ED). One proposed strategy to reduce exit block is early admission predictions by triage nurses to allow proactive bed management. We report a systematic review and meta-analysis of the accuracy of nurse prediction of admission at triage. METHODOLOGY: We searched MEDLINE, Cochrane, Embase, CINAHL and grey literature, up to and including February 2019. Our criteria were as follows: prospective studies analysing the accuracy of triage nurse intuition-after gathering standard triage information-for predicting disposition for adult ED patients. We analysed the results of this test-nurse prediction of disposition-in a diagnostic test analysis review style, assessing methodology with the Quality Assessment of Diagnostic Accuracy Studies 2 checklist. We generated sensitivity, specificity and likelihood ratios (LRs). We used LRs and pretest probability of admission (baseline admission rate) to find positive and negative post-test probabilities. RESULTS: We reviewed 10 articles. Of these, seven had meta-analysable data (12 282 participants). The studies varied in participant selection and admission rate, but the majority were of moderate quality and exclusion of each in sensitivity analyses made little difference. Sensitivity was 72% and specificity was 83%. Pretest probability of admission was 29%. Positive and negative post-test probabilities of admission were 63% and 12%, respectively. CONCLUSION: Triage nurse prediction of disposition is not accurate enough to expedite admission for ED patients on a one-to-one basis. Future research should explore the benefit, and best method, of predicting total demand.

Interpretable, not black-box, artificial intelligence should be used for embryo selection.

Artificial intelligence (AI) techniques are starting to be used in IVF, in particular for selecting which embryos to transfer to the woman. AI has the potential to process complex data sets, to be better at identifying subtle but important patterns, and to be more objective than humans when evaluating embryos. However, a current review of the literature shows much work is still needed before AI can be ethically implemented for this purpose. No randomized controlled trials (RCTs) have been published, and the efficacy studies which exist demonstrate that algorithms can broadly differentiate well between 'good-' and 'poor-' quality embryos but not necessarily between embryos of similar quality, which is the actual clinical need. Almost universally, the AI models were opaque ('black-box') in that at least some part of the process was uninterpretable. This gives rise to a number of epistemic and ethical concerns, including problems with trust, the possibility of using algorithms that generalize poorly to different populations, adverse economic implications for IVF clinics, potential misrepresentation of patient values, broader societal implications, a responsibility gap in the case of poor selection choices and introduction of a more paternalistic decision-making process. Use of interpretable models, which are constrained so that a human can easily understand and explain them, could overcome these concerns. The contribution of AI to IVF is potentially significant, but we recommend that AI models used in this field should be interpretable, and rigorously evaluated with RCTs before implementation. We also recommend long-term follow-up of children born after AI for embryo selection, regulatory oversight for implementation, and public availability of data and code to enable research teams to independently reproduce and validate existing models.

Tissue repair in neonatal and paediatric surgery: Analysis of infection in surgical implantation of synthetic resorbable biomaterials.

BACKGROUND: There has been increased interest in the use of biomaterials that resorb completely leaving only the patient's native tissue. Synthetic materials are advantageous for tissue repair because they are highly customisable. The infection rate of using resorbable natural materials in paediatric surgery has recently been outlined, but there has not yet been a review of the use of synthetic resorbable materials in paediatric surgery. OBJECTIVES: This systematic review analyses the risk of infection after implantation of fully resorbable synthetic biomaterials in paediatric cases. METHODS: The literature was searched from January 1970 to January 2018 (inclusive), specifically searching for paediatric cases (0-18 years old), use of synthetic resorbable materials and infection. RESULTS: The infection rate in 3573 cases of synthetic resorbable material implantation was 1.1% (41 cases). A Chi-squared test for independence found infection rate to vary among materials. Of the many biomaterials identified in this review, the highest infection rates were seen in Suprathel's use in burns injuries (12.1%). CONCLUSIONS: This review found a low infection rate in synthetic resorbable materials used in paediatric surgery, with particularly strong evidence for low infection risk in LactoSorb® use.