Evidence map›Paper›PMID 14521649›Full record

ReviewJournal of general internal medicine2003

Review of evidence and explanations for suboptimal screening and treatment of dyslipidemia in women. A conceptual model.

Catherine Kim, Timothy P Hofer, Eve A Kerr

Abstract readReview
In one paragraph

Review in Journal of general internal medicine, 2003. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Trial
  2. Article
  3. Review
  4. Article
  5. Observational
  6. Article
  7. Article
  8. Article
  9. Article
  10. Observational
  11. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Catherine KimDivision of General Internal Medicine, University of Michigan, Ann Arbor, MI, USA. cathkim@umich.edu
Timothy P Hofer
Eve A Kerr

Funding

Targeting Interventions to Reduce ErrorsP20HS011540 · AHRQ · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI HOFER, TIMOTHY P · 2001 to 2003
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AHRQ HHS 1P20HS011540-01AHRQ HHS P20 HS011540
6 · The paper itself

Abstract

Screening and treatment rates for dyslipidemia in populations at high risk for cardiovascular disease (CVD) are inappropriately low and rates among women may be lower than among men. We conducted a review of the literature for possible explanations of these observed gender differences and categorized the evidence in terms of a conceptual model that we describe. Factors related to physicians' attitudes and knowledge, the patient's priorities and characteristics, and the health care systems in which they interact are all likely to play important roles in determining screening rates, but are not well understood. Research and interventions that simultaneously consider the influence of patient, clinician, and health system factors, and particularly research that focuses on modifiable mechanisms, will help us understand the causes of the observed gender differences and lead to improvements in cholesterol screening and management in high-risk women. For example, patient and physician preferences for lipid and other CVD risk factor management have not been well studied, particularly in relation to other gender-specific screening issues, costs of therapy, and by degree of CVD risk; better understanding of how available health plan benefits interact with these preferences could lead to structural changes in benefits that might improve screening and treatment.

Indexed as

Health Knowledge, Attitudes, PracticeWomen's HealthCommunicationFemaleHealth Services AccessibilityHumansHyperlipidemiasMass ScreeningModels, OrganizationalRisk FactorsSex FactorsSocioeconomic FactorsUnited States

Identifiers

PMID14521649
PMCPMC1494935

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.