Evidence map›Paper›PMID 24627777›Full record

ArticleF1000Research2013

Paging Doctor Google! Heuristics vs. technology.

Kenar D Jhaveri, Peter B Schrier, Joseph Mattana

Abstract read
In one paragraph

Article in F1000Research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Trial
  2. 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.

Kenar D JhaveriDepartment of Internal Medicine, North Shore University Hospital and Long Island Jewish Medical Centre, Hofstra North Shore LIJ School of Medicine, Great Neck, NY, 11021, USA.
Peter B SchrierDepartment of Internal Medicine, North Shore University Hospital and Long Island Jewish Medical Centre, Hofstra North Shore LIJ School of Medicine, Great Neck, NY, 11021, USA.
Joseph MattanaDepartment of Internal Medicine, Winthrop-University Hospital, Mineola, NY, 11501, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The most dramatic development in medical decision-making technology has been the advent of the Internet. This has had an impact not only on clinicians, but has also become an important resource for patients who often approach their doctors with medical information they have obtained from the Internet.  Increasingly, medical students, residents and attending physicians have been using the Internet as a tool for diagnosing and treating disease. Internet-based resources that are available take various forms, including informational websites, online journals and textbooks, and social media.  Search engines such as Google have been increasingly used to help in making diagnoses of disease entities. Do these search methods fare better than experienced heuristic methods? In a small study, we examined the comparative role of heuristics versus the 'Google' mode of thinking. Internal medicine residents were asked to "google" key words to come up with a diagnosis. Their results were compared to experienced nephrology faculty and fellows in training using heuristics and no additional help of internet. Overall, with the aid of Google, the novices (internal medicine residents) correctly diagnosed renal diseases less often than the experts (the attendings) but with the same frequency as the intermediates (nephrology fellows).  However, in a subgroup analysis of both common diseases and rare diseases, the novices correctly diagnosed renal diseases less often than the experts but more often than the intermediates in each analysis.  The novices correctly diagnosed renal diseases with the same frequency as nephrology fellows in training.

Identifiers

PMID24627777
PMCPMC3907161

What OpenQuestion holds

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Registered trials

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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.