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Since "first, do no harm" remains a guiding principle of care, let's remember that the harm of missing a chance to help often greatly exceeds the harm of prescribing under a false pretext.
Each woman deserves to be given the choice by her medical team: Is she willing to accept the "harm of a false positive" in order to avoid or minimize the very real and devastating harm of missing an early diagnosis of a potentially life-threatening cancer?
In the case of the test that was more specific, but less sensitive, than the standard, its value depends on the harm of missing a case of disease relative to the harm of treating a patient unnecessarily.
We also provide a decision curve analysis based on the net benefit Net benefit= true positives−w×false positives)/n, where n is the sample size and the weight w represents the ratio between the harm of unnecessary treatment and the harm of missing a case at that given value of predicted risk.
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30 In an exploratory study using case scenarios of potentially harmful clinical situations, Lyndon et al 14 found that nurses' and physicians' perceptions of potential harm of missed hand disinfection differed considerably and were strongly linked to likelihood of speaking up.
Judgements are required that the benefits of avoiding unnecessary treatment in true negative findings outweigh the harms of missed or delayed treatment in false negatives.
This method estimates the "net benefit" of using a prediction model by summing the benefits (true positives) and subtracting the harms (false positives), where the latter is weighted by a factor related to the relative harm of a missed cancer compared to an unnecessary biopsy.
Decision curve analysis estimates a 'net benefit' for prediction models by summing the benefits (true positives) and subtracting the harms (false positives), where the latter is weighted by a factor so as to reflect the relative harm of a missed cancer compared with an unnecessary biopsy.
The principle findings of the study showed a high number of reviews suspected of outcome reporting bias as a result of missing or partially reported harm related outcomes.
This increases the risk of missing low levels of benefit or harm, which were not large enough to be statistically relevant in any single RCT.
"But recognition of this harm is missing from the conversation about criminal justice reform".
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com