Title |
Diffuse lung disease of infancy: a pattern-based, algorithmic approach to histological diagnosis
|
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Published in |
Journal of Clinical Pathology, December 2014
|
DOI | 10.1136/jclinpath-2014-202685 |
Pubmed ID | |
Authors |
Jane E Armes, William Mifsud, Michael Ashworth |
Abstract |
Diffuse lung disease (DLD) of infancy has multiple aetiologies and the spectrum of disease is substantially different from that seen in older children and adults. In many cases, a specific diagnosis renders a dire prognosis for the infant, with profound management implications. Two recently published series of DLD of infancy, collated from the archives of specialist centres, indicate that the majority of their cases were referred, implying that the majority of biopsies taken for DLD of infancy are first received by less experienced pathologists. The current literature describing DLD of infancy takes a predominantly aetiological approach to classification. We present an algorithmic, histological, pattern-based approach to diagnosis of DLD of infancy, which, with the aid of appropriate multidisciplinary input, including clinical and radiological expertise and ancillary diagnostic studies, may lead to an accurate and useful interim report, with timely exclusion of inappropriate diagnoses. Subsequent referral to a specialist centre for confirmatory diagnosis will be dependent on the individual case and the decision of the multidisciplinary team. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Spain | 2 | 50% |
Unknown | 2 | 50% |
Demographic breakdown
Type | Count | As % |
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Science communicators (journalists, bloggers, editors) | 3 | 75% |
Practitioners (doctors, other healthcare professionals) | 1 | 25% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United Kingdom | 1 | 2% |
South Africa | 1 | 2% |
Unknown | 45 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 9 | 19% |
Other | 7 | 15% |
Lecturer > Senior Lecturer | 4 | 9% |
Professor | 4 | 9% |
Student > Postgraduate | 4 | 9% |
Other | 13 | 28% |
Unknown | 6 | 13% |
Readers by discipline | Count | As % |
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Medicine and Dentistry | 30 | 64% |
Agricultural and Biological Sciences | 2 | 4% |
Veterinary Science and Veterinary Medicine | 1 | 2% |
Nursing and Health Professions | 1 | 2% |
Unspecified | 1 | 2% |
Other | 2 | 4% |
Unknown | 10 | 21% |