Demo corpus. Scores are computed on a select set of biomedical paper/datasets and may be inaccurate for papers outside this corpus — DataRank relies on network effects that improve with scale. We aim to expand this into a fully open resource pending additional funding.

Personalized Medicine

The International Encyclopedia of Health Communication(2022)10.1002/9781119678816.iehc0798Source: DataRank Database

Personalized Medicine is a research paper published in The International Encyclopedia of Health Communication (2022). On theSindex it has a DataRank of 0.208. It has been cited 3 times, with 3 citing works in its 1-hop citation network.

N/A

0.208DataRank · unranked

0.208

3 citations · base score 1.4

Cite:

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Abstract

“Personalized medicine” and the related concepts of “precision medicine” and “precision health” refer to the idea that healthcare approaches should not be one‐size‐fits‐all but instead tailored to a person's unique biological, behavioral, and environmental factors. A central focus of personalized medicine is genomic testing to generate more accurate disease risk profiles and to guide gene‐targeted therapies (i.e., pharmacogenomics). Personalized medicine also relies on individuals' digital health engagement and on healthcare informatics to generate personalized guidance. Health communication scholars have an important role to play in the success of personalized medicine. From research to clinical implementation, effective communication is key to enabling and implementing the discoveries that support a personalized approach to health. Important areas for communication research include research participant recruitment, digital health engagement and bioinformatics, conveying complex health information to patients and consumers, and the portrayal of personalized medicine in the media.

›Data sources & pipeline

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Pending

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (0/2)

Interoperable (0/2)

Reusable (0/3)

FAIR checklist signals are shown for context only and do not affect DataRank scoring.

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DataRank Breakdown

Base Score 100%Citation Network 0%

Base Score Contribution

0.208

From this paper's citation signal

Citation Network Contribution

From 0 citing papers with measurable signal

This paper's DataRank is currently driven only by its base citation score. None of the citing papers had measurable citation signal.

Learn more about DataRank methodology →

Why this DataRank?

DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.

Base score B(p): log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
Network N(p): Σ over citers of log1p(C_q) ÷ max(outdegree_q, 1). Being cited by a highly-cited paper with few references counts most.
Damping factor d = 0.85: DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
Self-citations excluded: Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.

Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.

Read the full methodology →