Evidence map›Paper›PMID 39240906›Full record

ArticlePloS one2024

Item response theory model highlighting rating scale of a rubric and rater-rubric interaction in objective structured clinical examination.

Masaki Uto, Jun Tsuruta, Kouji Araki, Maomi Ueno

Abstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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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.

Masaki UtoDepartment of Computer and Network Engineering, The University of Electro-Communications, Chofu, Tokyo, Japan.ORCID 0000-0002-9330-5158
Jun TsurutaInstitute of Education, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo, Japan.
Kouji ArakiEducational System in Dentistry, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo, Japan.
Maomi UenoDepartment of Computer and Network Engineering, The University of Electro-Communications, Chofu, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective structured clinical examinations (OSCEs) are a widely used performance assessment for medical and dental students. A common limitation of OSCEs is that the evaluation results depend on the characteristics of raters and a scoring rubric. To overcome this limitation, item response theory (IRT) models such as the many-facet Rasch model have been proposed to estimate examinee abilities while taking into account the characteristics of raters and evaluation items in a rubric. However, conventional IRT models have two impractical assumptions: constant rater severity across all evaluation items in a rubric and an equal interval rating scale among evaluation items, which can decrease model fitting and ability measurement accuracy. To resolve this problem, we propose a new IRT model that introduces two parameters: (1) a rater-item interaction parameter representing the rater severity for each evaluation item and (2) an item-specific step-difficulty parameter representing the difference in rating scales among evaluation items. We demonstrate the effectiveness of the proposed model by applying it to actual data collected from a medical interview test conducted at Tokyo Medical and Dental University as part of a post-clinical clerkship OSCE. The experimental results showed that the proposed model was well-fitted to our OSCE data and measured ability accurately. Furthermore, it provided abundant information on rater and item characteristics that conventional models cannot, helping us to better understand rater and item properties.

Indexed as

Educational MeasurementClinical CompetenceHumansModels, TheoreticalStudents, DentalStudents, Medical

Identifiers

PMID39240906
PMCPMC11379165

What OpenQuestion holds

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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.