Major health consequences of a changing climate are predicted. The importance of individual action to counter climatic changes and their effects on health is emphasized here.
The “fragility index” for a clinical trial reports how robust the results of the trial are. It calculates the minimum number of patients who would need to have a different primary outcome to change the measured p value from < 0.05 to > 0.05. A low fragility index means that only a small number of patients need to have a different outcome for the trial to lose traditional statistical significance. This increases the probability that the trial result was due to the play of chance. The application of this principle is described here for placebo-controlled trials of thrombolysis with alteplase (tPA) in acute stroke.
Tasman Medical Journal 2020; 2(1): 6-10
It is not possible to consent a patient for emergency research in advance of their acute illness, as the clinical events are not predictable. We discuss the problems of undertaking research in an emergency care setting, where patients lack the capacity to provide consent but require time critical interventions. There is no settled definition of what constitutes ‘research’ as distinct from ‘treatment’. In fact their relationship is a continuum that demands similar level of oversight for both. We provide examples where the usual requirement for written informed consent means the trial underestimated benefits, because the treatment would normally be given without delay. Consent rituals that delay the start of trial treatments may in fact be unethical and offend against the Declaration of Helsinki. Clinical research delivers improved patient outcomes irrespective of the randomised group. This is currently under threat by a lack of consistency across jurisdictions in the interpretation of the relevant legislation. Tasman Medical Journal 2020; 2(1): 11-14
Objective Research in artificial intelligence area appears to have been taken up by different specialties with varying enthusiasm. We compared the contribution of different medical specialties to machine learning, deep learning, and artificial intelligence research over 30 years.
Methods The Web of Science database was searched retrospectively for the terms “artificial intelligence”, “machine learning” and “deep learning”. Results were limited to articles, proceedings papers, or reviews published in Web of Science categories mapped to the OECD 3.02 Clinical Medicine schema between the year 1988 and 2018. A list of international medical specialties was assembled, a search tool was created to query author affiliation data, and analysis performed to assess specialty publication over time and inter-specialty collaboration. Publication differences between specialties were evaluated for significance with two-tail unpaired t-test.
Results Initial database search returned 3937 unique results once duplicates were removed. Medical specialty analysis returned 2381 papers from 789 different journals. Radiology published significantly more papers than other top specialties (p < 0.001), being involved in 783 papers (33% of returned total), followed by psychiatry (406 papers) and neurology (287 papers).
Conclusion There has been an exponentially increase in yearly publications involving artificial intelligence, machine learning, and deep learning over the last 30 years. Radiology is the leading medical speciality in machine learning, deep learning, and artificial intelligence research in terms of volume of yearly publications and overall citations, followed by psychiatry and neurology.
Tasman Medical Journal 2020; 2: 20-27
Background The Australian Therapeutic Guidelines does not endorse culture and susceptibility testing prior to salvage therapy for Helicobacter pylorieradication. We wished to determine whether this remains appropriate.
Aim To determine the sensitivity (as minimum inhibitory concentrations, MIC) of H pylorito a range of antibiotics used in salvage therapy over time.
Methods From 2012 to 2017, gastric or duodenal biopsy samples were obtained from 154 patients receiving H pylorieradication therapy. MIC for amoxicillin, clarithromycin, tetracycline, metronidazole, rifampicin and levofloxacinwere measured using standard laboratory techniques.
Results A significant increase from zero to 28% in secondary resistance to levofloxacin amongst H. pyloriin Western Australia was noted over the study period. No corresponding trend was seen with the other antibiotics.
Conclusions These findings suggest that selective use of culture and susceptibility testing may be warranted prior to initiating salvage therapy with levofloxacin. Tasman Medical Journal 2020; 2: 15-19
Adverse events are common after non-cardiac surgery and are associated with increasedperioperative mortality and morbidity. Risk stratification is vital to minimize those adverse outcomes and also facilitates informed decision-making by patients when consenting. The use of biomarkers in risk stratification has been discussed in the literature. Amongst them, brain natriuretic peptide (BNP) has been studied widely as a predictor of perioperative adverse outcomes. However, there is no global consensus on its use. The aim of this review is to discuss evidence and guidelines on the use of BNP in perioperative risk stratification prior to non-cardiac surgery. Tasman Medical Journal 2020; 2: 28-33.
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