Evidence map›Paper›PMID 28420347›Full record

ArticleBMC medical research methodology2017

Kappa statistic to measure agreement beyond chance in free-response assessments.

Marc Carpentier, Christophe Combescure, Laura Merlini, Thomas V Perneger

Abstract read
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Article in BMC medical research methodology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

Marc CarpentierDivision of Clinical Epidemiology, Geneva University Hospitals, and Faculty of Medicine, University of Geneva, Geneva, Switzerland.
Christophe CombescureDivision of Clinical Epidemiology, Geneva University Hospitals, and Faculty of Medicine, University of Geneva, Geneva, Switzerland.
Laura MerliniDivision of Radiology, Geneva University Hospitals, and Faculty of Medicine, University of Geneva, Geneva, Switzerland.
Thomas V PernegerDivision of Clinical Epidemiology, Geneva University Hospitals, and Faculty of Medicine, University of Geneva, Geneva, Switzerland. thomas.perneger@hcuge.ch.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe usual kappa statistic requires that all observations be enumerated. However, in free-response assessments, only positive (or abnormal) findings are notified, but negative (or normal) findings are not. This situation occurs frequently in imaging or other diagnostic studies. We propose here a kappa statistic that is suitable for free-response assessments.

methodWe derived the equivalent of Cohen's kappa statistic for two raters under the assumption that the number of possible findings for any given patient is very large, as well as a formula for sampling variance that is applicable to independent observations (for clustered observations, a bootstrap procedure is proposed). The proposed statistic was applied to a real-life dataset, and compared with the common practice of collapsing observations within a finite number of regions of interest.

resultsThe free-response kappa is computed from the total numbers of discordant (b and c) and concordant positive (d) observations made in all patients, as 2d/(b + c + 2d). In 84 full-body magnetic resonance imaging procedures in children that were evaluated by 2 independent raters, the free-response kappa statistic was 0.820. Aggregation of results within regions of interest resulted in overestimation of agreement beyond chance.

conclusionsThe free-response kappa provides an estimate of agreement beyond chance in situations where only positive findings are reported by raters.

Indexed as

Magnetic Resonance ImagingStatistics as TopicChildDatasets as TopicHumansObserver VariationBiostatisticsMethodological StudyReliability (Epidemiology)Reproducibility of results

Identifiers

PMID28420347
PMCPMC5395923

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