Publications by authors named "Peter Twining"

3 Publications

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Machine learning prediction models in orthopedic surgery: A systematic review in transparent reporting.

J Orthop Res 2021 Mar 18. Epub 2021 Mar 18.

Orthopedic Oncology Service, Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.

Machine learning (ML) studies are becoming increasingly popular in orthopedics but lack a critically appraisal of their adherence to peer-reviewed guidelines. The objective of this review was to (1) evaluate quality and transparent reporting of ML prediction models in orthopedic surgery based on the transparent reporting of multivariable prediction models for individual prognosis or diagnosis (TRIPOD), and (2) assess risk of bias with the Prediction model Risk Of Bias ASsessment Tool. A systematic review was performed to identify all ML prediction studies published in orthopedic surgery through June 18th, 2020. After screening 7138 studies, 59 studies met the study criteria and were included. Two reviewers independently extracted data and discrepancies were resolved by discussion with at least two additional reviewers present. Across all studies, the overall median completeness for the TRIPOD checklist was 53% (interquartile range 47%-60%). The overall risk of bias was low in 44% (n = 26), high in 41% (n = 24), and unclear in 15% (n = 9). High overall risk of bias was driven by incomplete reporting of performance measures, inadequate handling of missing data, and use of small datasets with inadequate outcome numbers. Although the number of ML studies in orthopedic surgery is increasing rapidly, over 40% of the existing models are at high risk of bias. Furthermore, over half incompletely reported their methods and/or performance measures. Until these issues are adequately addressed to give patients and providers trust in ML models, a considerable gap remains between the development of ML prediction models and their implementation in orthopedic practice.
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http://dx.doi.org/10.1002/jor.25036DOI Listing
March 2021

Developing a quality curriculum in a technological era.

Educ Technol Res Dev 2020 Nov 11:1-24. Epub 2020 Nov 11.

Université Laval, Department of Foundations and Practices in Education, Québec, QC Canada.

There is considerable rhetoric internationally around the need for national curricula to reflect the changes that are taking place in the world outside school. This raises questions about what a quality curriculum in a technological era should look like, and equally challenging issues about how to achieve the necessary changes in schooling in order for such a curriculum to be realised. This paper summarises the views of 11 experts from seven countries. It introduces a sociocultural framework that highlights the complexity of achieving alignment between policies and practice spanning the national to local school to classroom levels. Three key issues that underpin alignment are then explored, each of which link with the issue of trust:stakeholders engagement;teacher professionalism;summative assessment. By exploring and exemplifying these three issues the paper indicates potential ways of addressing them and provides 'tools to think with' to enhance future curriculum development initiatives.
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http://dx.doi.org/10.1007/s11423-020-09857-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7657067PMC
November 2020

Would gestational age and presence of brain anomalies affect interobserver reliability of fetal head biometry? Using off-line analysis of 3-D dataset.

Ultrasound Med Biol 2012 Jan 21;38(1):69-74. Epub 2011 Nov 21.

Fetal and Maternal Medicine Department, Nottingham University Hospitals NHS Trust, Nottingham, UK.

The objective was to assess interobserver reliability of fetal head biometry using archived three-dimensional (3-D) volumes and the impact of gestational age and presence of brain anomalies on examiners' performance. Seventy nine 3-D volume datasets of fetal head were examined: 27 were normal and 52 had brain abnormalities. Off-line analysis was done by three fetal medicine experts (E1, E2 and E2), all were blinded to history and patient details. Measurements of the biparietal diameter (BPD), head circumference (HC), lateral ventricle (Vp) and transcerebellar diameter (TCD) were compared between examiners and to two-dimensional (2-D) measurements. Comparisons were made at two gestational age groups (≤22 and >22 weeks) and in presence and absence of brain anomalies. The intraclass coefficient showed a significantly high level of measurement agreement between 3-D examiners and 2-D, with values >0.9 throughout (p < 0.001). Bias was evident between 3-D examiners. E2 produced smaller measurements. The mean percentage difference between this examiner and the other two in BPD, HC, Vp and TCD measurements was significant, of 1.6%, 1%, 4.9% and 1.8%, respectively. E1 measured statistically larger for HC and TCD. E3 measured significantly larger for only BPD. The presence of anomalies was of no influence on the 3-D examiners' performance except for E3 who showed bias in BPD measurements only in cases with brain anomalies. Unlike other examiners, bias of E2 was only seen at gestational age group ≤22 weeks. Limits of agreement in measurements between observers were narrow for all parameters but were widest for the Vp measurements, being ±23% of the mean difference. Despite the above bias, the actual mean difference between examiners was small and unlikely to be of any clinical significance. Off-line measurement of fetal head biometry using 3-D volumes is reliable. In our study, presence of brain anomalies was unlikely to influence the reproducibility of measurements. Gestational age seemed to be of an impact on examiners' bias. Among experts this bias may be of no clinical significance.
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http://dx.doi.org/10.1016/j.ultrasmedbio.2011.10.016DOI Listing
January 2012