Biomedical subjects

C Meek

Publications and source records attributed to C Meek.

3 recordsLinked to original sources

PubMed2003

A systematic review of exercise trials post stroke.

OBJECTIVE: To perform a systematic review of exercise trials post stroke. DESIGN: A systematic review of controlled clinical trials. SEARCH STRATEGY: MEDLINE, EMBASE, CINAHL, Amed, Sports Discus, Cochrane controlled trials register and PEDro were searched for relevant trials. INCLUSION CRITERIA: Studies--randomized or quasi-randomized controlled clinical trials. Participants--Adults of any age with a clinical diagnosis of stroke. Interventions--Any cardiovascular exercise intervention aimed at improving cardiovascular fitness and/or function. OUTCOMES: Impairment: gait speed, strength, endurance, balance, flexibility, tonus and exercise capacity. Disability: global dependency, functional independence. Extended activities of daily living. Quality of life. Death. DATA COLLECTION AND ANALYSIS: Two independent reviewers categorized selected trials, documented the methodological quality and extracted the relevant data. Comparisons of cardiovascular exercise interventions versus no cardiovascular intervention were made. Statistical comparisons were carried out using a random effects model to calculate standardized mean differences. RESULTS: We identified three eligible trials. Small numbers and heterogeneous outcomes limited the analyses and comparisons. Based on the limited data available, we found that cardiovascular exercise post stroke was no better than no exercise with respect to disability, impairment, extended activities of daily living, quality of life and death. CONCLUSION: Insufficient evidence was identified to establish if cardiovascular exercise has a positive effect on disability, impairment, extended activities of daily living, quality of life and case fatality post stroke.

Activities of Daily Living↗

PubMed2002

Assessing the efficacy of a ram-genotyping programme to reduce susceptibility to scrapie in Great Britain.

Susceptibility to clinical scrapie is associated with polymorphisms in the PrP gene. The 'ARR' allele of this gene reduces susceptibility to clinical disease caused by all known strains of the transmissible spongiform encephalopathy (TSE) agent. The British government proposes to use a ram-genotyping scheme to breed genetic resistance to clinical scrapie into the national sheep population. We considered how best to target limited genotyping resources to achieve the maximum rate of genotype evolution. We created a metapopulation model of the British sheep industry, which includes the major pure-breeds of sheep and the cross-breeds produced by crossing these pure-bred animals. The main criterion for assessing the efficacy of different strategies was the time taken to increase the prevalence of the ARR allele in the slaughter-lamb population. Our model predicted that the most-effective strategy would be to target genotyping to those rams used for pure-breeding (i.e. mated with the same breed of ewe). This strategy was compared to two further strategies, in which the proportion of rams genotyped in each breed depended on the prevalence of the ARR/ARR genotype in that breed. A policy in which the proportion of animals genotyped is reduced as the ARR prevalence in that breed increases is efficient. The most-effective policy was targeting the hill sector in the early years and gradually switching to genotyping more terminal-sire and longwool rams as the resistance of the hill sector increases.

Animal Husbandry↗

PubMed1997

An evaluation of machine-learning methods for predicting pneumonia mortality.

This paper describes the application of eight statistical and machine-learning methods to derive computer models for predicting mortality of hospital patients with pneumonia from their findings at initial presentation. The eight models were each constructed based on 9847 patient cases and they were each evaluated on 4352 additional cases. The primary evaluation metric was the error in predicted survival as a function of the fraction of patients predicted to survive. This metric is useful in assessing a model's potential to assist a clinician in deciding whether to treat a given patient in the hospital or at home. We examined the error rates of the models when predicting that a given fraction of patients will survive. We examined survival fractions between 0.1 and 0.6. Over this range, each model's predictive error rate was within 1% of the error rate of every other model. When predicting that approximately 30% of the patients will survive, all the models have an error rate of less than 1.5%. The models are distinguished more by the number of variables and parameters that they contain than by their error rates; these differences suggest which models may be the most amenable to future implementation as paper-based guidelines.

Artificial Intelligence↗