Evidence map›Paper›PMID 25674142›Full record

ArticlePakistan journal of medical sciences

Development and validation of a convenient formula evaluating the value and applicability of medical literature in clinical practice.

Hsiao-Pei Mok, Ying Zhou, Jun-Ru Chen, Qiang Gao

Abstract read
In one paragraph

Article in Pakistan journal of medical sciences. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

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

What it found

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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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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

4 authors.

Hsiao-Pei MokHsiao-Pei Mok, West China Medical School, Sichuan University, Chengdu, Sichuan 610041, China.
Ying ZhouYing Zhou, West China Medical School, Sichuan University, Chengdu, Sichuan 610041, China.
Jun-Ru ChenJun-Ru Chen, West China Medical School, Sichuan University, Chengdu, Sichuan 610041, China.
Qiang GaoQiang Gao, Department of Cardiovascular Surgery, Guangdong General Hospital & Guangdong Cardiovascular Institute, Guangzhou, Guangdong 510100, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveEvidence-based medicine offers explicit methods to evaluate the evidence grades of literature. However, evidence grades do not meet all the practical needs of physicians. This study is aimed to develop a convenient method for evaluating the clinical value of medical literature from the perspective of the clinician.

methodsA literature applicability equation was formulated through the Delphi method and the analytic hierarchy process. A consistency check was used to ascertain the efficacy of the formula. Three senior clinicians assessed 30 articles based on their clinical experiences and subjective opinions, while one independent researcher performed independent assessments of the applicability of 30 articles using the evaluation formula.

resultsThe literature applicability equation was Y = 3.93X1 + 11.78X2 + 14.83X3 + 44.53X4 + 24.93X5, where Y = literature applicability, X1 = years since publication, X2 = target question covered or not, X3 = sample size, X4 = study type, and X5 = journal quality. Consistency index (CI) values for the first-level indicator ("literature applicability") and the second-level indicators ("pertinence and timeliness" and "quality of results") were 0.0325, 0.0012, and 0.0001, respectively. The weights used to calculate the matrix indicators had satisfactory accordance (random coincidence coefficient = 0.056). A consistency check for the efficacy of the formula revealed kappa = 0.749 and P < .001. Conclusion : The developed and validated literature applicability evaluation formula may be a useful and convenient tool for identifying clinically valuable medical literature.

Indexed as

Analytic hierarchy processDelphi methodEvaluation methodEvidence-based medicineLiterature applicabilityMedical literature

Identifiers

PMID25674142
PMCPMC4320734

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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.