The Waikato District Health Board (Waikato DHB) is a district health board with the focus on providing healthcare to the Waikato region of New Zealand.
Accurate measurement of ECG intervals, including PR, QRS duration, and QT/QTc, is central to cardiac diagnosis, yet the published ECG delineation literature evaluates performance almost exclusively as fiducial-point timing errors on small curated databases, rather than as clinical interval accuracy on large unselected cohorts. We bridge this gap by evaluating a complete end-to-end pipeline on 10,646 clinical 12-lead ECGs and reporting the first large-scale interval measurement accuracy study with full statistical characterisation, including bias, 95
In Aotearoa New Zealand, there is a strong commitment to providing clinical placements for undergraduate nursing students; however, sustaining the traditional one-to-one preceptorship model presents increasing operational and nursing workforce challenges. This integrative review, guided by Whittemore and Knafl’s methodology, aimed to identify effective preceptorship models that support undergraduate and postgraduate nursing student learning in clinical settings, alongside the dedicated one-to-one model. A comprehensive literature search was conducted across five databases (CINAHL, EBSCO, PubMed, Ovid and Discovery), focusing on primary research published in English between 2017 and 2024 that involved the key concepts of preceptorship of nursing students or nursing as precepetees in clinical settings. A total of 1446 articles were identified and following the removal of duplicates, application of inclusion/exclusion criteria and quality screening, 15 articles were selected for thematic analysis. Findings were grouped into three main models of preceptor allocation: 1) one preceptor to one student; 2) one preceptor to many students; and 3) many preceptors to one student. The findings highlight the need for flexible and responsive preceptorship models that can adapt to workforce demands while maintaining quality learning experiences. In conclusion, each model demonstrated distinct strengths and limitations shaped by the clinical context, with implications for how preceptor roles are structured and operationalised. To ensure effective clinical education, it is essential to strengthen preceptorship programmes through collaborative partnerships between academic institutions and clinical settings, with nurse educators playing a central role in designing and supporting these adaptable models. Te reo Māori translation Te tūhura tauira whakaakoranga hei whakapiki i te akoranga tiaki tūroro i te ao tapuhi: He arotake torowhānui Ngā Ariā Matua Ka nui te piripono o te tangata i Aotearoa ki te hora tūranga tiaki tūroro mō ngā ākonga tapuhi paetahi; ahakoa taua hiahia, kua uaua te kawe i taua tauira, nā te uaua o te kimi tūranga whakaako takitahi, nā ngā uauatanga hoki o te whakahaere, o te ohu kaimahi tapuhi. Ko tā tēnei arotake torowhānui, he mea ārahi e ngā tikanga rangahau a Whittemore rāua ko Knafl, he whai kia tautohutia ētahi tauira whakaako whai hua hei tautoko i te akoranga o ngā ākonga tapuhi paetahi me te paerua i ngā horopaki tiaki tūroro, i te taha o te tauira whakaako takitahi pū. I kawea tētahi rapunga tuhinga mā ētahi pātengi raraunga e rima (CINAHL, EBSCO, PubMed, Ovid me Discovery), i arotahi rā ki ngā rangahau taketake i whakaputaina ki te reo Ingarihi i waenga i 2017 me 2024, i uru atu ai ngā ariā matua o te whakaakoranga o ngā ākonga tapuhi, me te noho o ngā ākonga hei ākonga i roto i ngā horopaki tiaki tūroro. I tautohutia ētahi tuhinga 1446, ā, i muri i te mukunga o ngā mea taurite, me te hoatuanga o ngā paearu whakauru/aukati, me te tātaritanga kounga, 15 ngā tuhinga i tīpokangia mō te tātari tāhuhu. I rohea ngā kitenga kia rere ētahi tauira matua e toru mō te momo kaiwhakaako: 1) kotahi kaiako mō te ākonga kotahi; 2) kotahi kaiako mō ngā ākonga maha; ā, 3) he maha ngā kaiako mā te kaiako kotahi. Nā ngā kitenga ka mōhio pea tātou me kimi ētahi tauira pīngawingawi, atamai hoki, kakama hoki te urutau ki ngā hiahia o te ohu kaimahi, me te whakaputa wheako akoranga kounga nui. Hei kupu whakamutunga, ko tā ia tauira he hora i ngā kahanga me ngā ngoikoretanga i takea mai i te horopaki tiaki tūroro, me ngā tikanga ka puta i muri mō te whakatāhuhu me te whakatinana i ngā tūranga whakaako. E puta ai he akoranga tiaki tūroro whai take, me mātua whakapakari ngā hōtaka whakaako, mā ngā pātuitanga pāhekoheko i waenga i ngā whare whakaako tiketike me ngā horopaki tiaki tūroro, me te noho mai o ngā kaiako tapuhi ki te kawe i ngā mahi nunui o te hoahoa me te tautoko i ēnei tauira ka taea te whakaurutau.
Atrial fibrillation (AFib) represents a critical diagnostic challenge in clinical cardiology, calling for automated detection systems capable of robust performance across diverse clinical environments. We present a computationally efficient deep neural network architecture for AFib detection that demonstrates exceptional generalizability despite training on a modest dataset. Our convolutional neural network, comprising 17 million parameters, was trained on 67,432 12-lead electrocardiograms and subsequently validated on over 1.1 million ECG recordings spanning six independent public datasets. On CPU, the model processes a 10-second ECG in 200 ms, enabling real-time inference capabilities. Our model achieves state-of-the-art performance, with an average area under the receiver operating characteristic curve (AUROC) of 0.97, an average sensitivity of 0.83, and an average specificity of 0.96 across six external validation cohorts. These metrics rank among the highest reported in the literature, while preserving computational efficiency suitable for resource-constrained environments. A key innovation of our approach is the implementation of channel-masking methodology, enabling seam- less operation across variable lead configurations without model retraining. This flexibility allows deployment from single-lead ambulatory monitors to comprehensive 12-lead clinical systems using identical network weights. Gradient based saliency analysis confirms the models attention to physiologically relevant features, particularly P-wave morphology and lead II characteristics, thereby enhancing clinical interpretability and trustworthiness. Our findings also establish that a single, well curated small training dataset can yield a compact yet highly generalizable AFib detection system suitable for deployment across diverse clinical settings, from critical care monitoring to ambulatory screening applications. The combination of robust cross-dataset performance, computational efficiency, and clinical interpretability positions this approach as a viable solution for large scale AFib diagnosis and monitoring programs. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present work are contained in the manuscript
