ArticleBMJ open quality2023
History information management strategy for minimising biases and noise for improved medical diagnosis.
Article in BMJ open quality, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite measures for physicians' excellence in diagnosis, the need for improvement of medical history techniques has been pointed out as one of the critical elements for improving diagnosis. Specific and proactive frameworks related to methods of effective history acquisition are needed to minimise bias and optimise decision-making. Therefore, this paper uses Linear Sequential Unmasking- Expanded to develop and propose a structured medical history acquisition strategy. The strength of this lies in its reliance on cognitive psychological processes. Breaking information gatherings and decisions into smaller tasks and ordering them correctly reduces cognitive load as well as minimises noise and bias cascade. Additionally, this approach can help physicians develop diagnostic expertise regardless of specialty.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.