Comment on: “Zoo or Savannah? Choice of Training Ground for Evidence-Based Pharmacovigilance”
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To ensure relevance and avoid trivial classification, non-cases (controls) were limited to patients with more than one visit at Stanford, at least one visit within the last 2 years, and one comorbidity from hypertension, coronary atherosclerosis, dyslipidemia, or myocardial infarction.
“To ensure relevance of our classifier for use on patients at a higher risk for ASCVD for discerning between FH cases and similar non-FH cases “in the wild”,37,38 and to ensure that trivial classification (e.g., healthy 25-year olds classified as non-FH) does not produce optimistic results, we limited our non-cases (“controls”) to patients that: (1) had more than one visit at Stanford; (2) had at l”
From Finding missed cases of familial hypercholesterolemia in health systems using machine learning (2019) · cites this paper as [38]
The citation is attached to the rationale for evaluating a classifier on higher-risk patients ‘in the wild’; the control-selection rules are the citing authors’ own methods. The cited paper’s To the Editor section discusses the real-world relevance of pharmacovigilance benchmarks, but not FH classification, ASCVD risk, Stanford patients, or the stated control criteria. Searches for those terms found no supporting passage in our copy. (LLM, conf=0.78)
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Research works printed but not linked: 7
- [1]Noren GN Caster O Juhlin K Lindquist M Zoo or savannah? Choice of training ground for evidence-based pharmacovigilance Drug Saf 9 2014 37 9 655 659 25005708
- [2]Ryan PB Schuemie MJ Welebob E Duke J Valentine S Hartzema AG Defining a reference set to support methodological research in drug safety Drug Saf 10 2013 36 Suppl 1 S33 47 24166222
- [3]Coloma PM Avillach P Salvo F A Reference Standard for Evaluation of Methods for Drug Safety Signal Detection Using Electronic Healthcare Record Databases Drug-Safety 1 2013 36 1 13 23 23315292
- [4]Harpaz R Odgers D Gaskin G A Time-indexed Reference Standard of Adverse Drug Reactions Nature Scientific Data 2014 in press PMC4306188 25632348
- [5]Harpaz R Dumouchel W Lependu P Bauer-Mehren A Ryan P Shah NH Performance of pharmacovigilance signal-detection algorithms for the FDA adverse event reporting system Clinical pharmacology and therapeutics 6 2013 93 6 539 546 23571771 PMC3857139
- [6]Boyce RD Ryan PB Noren GN Bridging islands of information to establish an integrated knowledge base of drugs and health outcomes of interest Drug Saf 8 2014 37 8 557 567 24985530 PMC4134480
- [7]LePendu P Iyer SV Bauer-Mehren A Pharmacovigilance using clinical notes Clinical pharmacology and therapeutics 6 2013 93 6 547 555 23571773 PMC3846296
Cited by
Sorted by citations
Finding missed cases of familial hypercholesterolemia in health systems using machine learning
Banda, J. M., Sarraju, A., Abbasi, F., Parizo, J., Pariani, M., Ison, H., Briskin, E., Wand, H., Dubois, S., Jung, K., et al. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0101-5 · Cited by 123 · see citing works
Self-citation
- Authors
- Harpaz, R., DuMouchel, W., & Shah, N. H.
- Affiliations
- Stanford University (US)
- Corresponding author
- Harpaz, Rave
- ORCID
- Shah, Nigam H.
- Venue
- Drug Safety
- Published
- 2014-11-29
- Volume
- 38
- Issue
- 1
- Pages
- 113-114
- ISSN
- 0114-5916 (ISSN-L)
- 1179-1942
- PMC
- PMC4706737
- OpenAlex
- W2000054710
- Cited by
- 9
- References
- 0
- Citations by year
- 2023: 1 · 2022: 1 · 2019: 1 · 2018: 1 · 2016: 2 · 2015: 2 · 2014: 1
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Drug Safety (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Human Genome Research Institute (U54HG004028)
- National Institute of General Medical Sciences (GM101430- 01A1, R01GM101430)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Toxicology › Pharmacovigilance and Adverse Drug Reactions
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmaceutical industry and healthcare
- Keywords
- pharmacovigilance, signal detection, benchmark, FDA Adverse Event Reporting System, OMOP, adverse drug reactions, positive and negative controls, prospective evaluation, spontaneous reporting
- MeSH terms
- Humans, Adverse Drug Reaction Reporting Systems, Benchmarking
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