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Use of reclassification for assessment of improved prediction: an empirical evaluation

International Journal of Epidemiology(2011)10.1093/ije/dyr013Source: DataRank Database

Use of reclassification for assessment of improved prediction: an empirical evaluation is a research paper published in International Journal of Epidemiology (2011). On theSindex it has a DataRank of 0.601. It has been cited 54 times.

N/A
0.601DataRank · unranked
0.601
Open Access54 citations · base score 4.0
Cite:
datarank_citation_only_1hop_v6· scope data_onlyMethodology

Abstract

BackgroundAn increasing number of studies evaluate the ability of predictors to change risk stratification and alter medical decisions, i.e. reclassification performance. We examined the reported design and analysis of recent studies of reclassification and the robustness of their claims for improved reclassification.MethodsTwo independent investigators searched PubMed and citations to the article that introduced the currently most popular reclassification metric (net reclassification index, NRI) to identify studies performing reclassification analysis (January 2006-January 2010). We focused on articles that included any analyses comparing the performance of a baseline predictive model vs the baseline model plus some additional predictor for a prospectively assessed outcome. We recorded information on the baseline model used, outcomes assessed, choice of risk thresholds and features of reclassification analyses.ResultsOf 58 baseline models used in 51 eligible papers, only 14 (24%) were previously described, used as described and had same outcomes as originally intended. Calibration was examined in 53% of the studies. Sixteen studies (31%) provided a reference for the choice of risk thresholds and only six used the previously proposed categories or justified the use of alternative thresholds. Only 14 studies (27%) stated that the chosen risk thresholds had different therapeutic intervention implications. NRI was calculated in 38 studies and was smaller in studies with adequately referenced or justified risk thresholds vs others (P ConclusionsReclassification studies would benefit from more rigorous methodological standards; otherwise claims for improved reclassification may remain spurious.

Data sources & pipeline
Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring
Enrichment:Pending

FAIR Checklist

Context only (not used in score)
Findable (1/2)
  • Has DOI
Accessible (1/2)
  • Open Access
Interoperable (0/2)
    Reusable (0/3)

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

      DataRank Breakdown

      Base Score 100%Citation Network 0%

      Base Score Contribution

      0.601

      From this paper's citation signal

      Citation Network Contribution

      0

      Citation network not refreshed for this result

      This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.

      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(Cq) ÷ max(outdegreeq, 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 →

      Related Papers (10)

      Cancer Epidemiology, Biomarkers & Prevention(2013)
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      10.1158/1055-9965.epi-13-0146
      Epidemiology(2008)
      co-cited
      10.1097/ede.0b013e31818131e7