A flurry of interesting journal articles.
From the October CAP TODAY, two cover articles. One on "the art and science of positive blood cultures," which may sound like a yawner, but it's about the range of new molecular and fluidic biotechnologies that are changing the field of clinical microbiology, especially in the area of rapid upfront results. See also "checklists now made to fit for NGS labs," about updates and new paradigms for CAP policies and reviews for NGS. Find it here.
I've previously remarked on how much cool stuff appears in JACR, Journal of the American College of Radiology. From the October issue:
- Vachani et al. Complications after biopsy of pulmonary nodules. Complications rate 25%, dominantly pneumothorax, of which, 32% (8% of total) require a chest tube. Here. Open access.
- Pair with an in press article on costs of lung biopsy, Tailor et al., here.
- These are relevant to all the developers of tests to biologically predict the risk of cancer in a small ambiguous imaging nodule. One test of this type, Biodesix CL2, 0080U, had 1700 Medicare uses in 2021, for $6M in payments (here).
- Hendrix et al. Radiologist preferences for AI-based decision support during screening mammography. Here. Open access.
- This is a very original, very detailed article on different preferences and needs of radiologists for AI in screening imaging. See esp. Table 1 for their approach.
- Lab medicine: My point here is, this approach could be a model for thinking about topics like digital pathology AI, and even for physician thinking, use and interpretation of NGS reports (aside from AI).
- Makhnevich et al. Comparison of chest radiograph impressions for diagnosing pneumonia: Categories of language uncertainty. Here.
- We have uncertainty in anatomic pathology and cytology, as well as molecular medicine and genetics (consider also VUS).
- This is the most elaborate and structured paper I have seen about levels of uncertainty.
- Ho et al. Prioritizing screening mammograms for immediate interpretation and diagnostic evaluation, based on : risk of recall. (Meaning, risk of calling patient in for more studies). Here. Open access.
- Obviously the potential for a notable contribution to patient/physician service and public health.
- Adleberg et al. Predicting patient demographics from chest radiographs with deep learning. Here.
|"Standard" visual abstract designed by journal for selected pub's (JACR)|