Dataset Information

Identifying main finding sentences in clinical case reports.

ABSTRACT: Clinical case reports are the 'eyewitness reports' of medicine and provide a valuable, unique, albeit noisy and underutilized type of evidence. Generally, a case report has a single main finding that represents the reason for writing up the report in the first place. However, no one has previously created an automatic way of identifying main finding sentences in case reports. We previously created a manual corpus of main finding sentences extracted from the abstracts and full text of clinical case reports. Here, we have utilized the corpus to create a machine learning-based model that automatically predicts which sentence(s) from abstracts state the main finding. The model has been evaluated on a separate manual corpus of clinical case reports and found to have good performance. This is a step toward setting up a retrieval system in which, given one case report, one can find other case reports that report the same or very similar main findings. The code and necessary files to run the main finding model can be downloaded from https://github.com/qi29/main_ finding_recognition, released under the Apache License, Version 2.0.

SUBMITTER: Luo M

PROVIDER: S-EPMC7287507 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Identifying main finding sentences in clinical case reports.

Luo Mengqi M Cohen Aaron M AM Addepalli Sidharth S Smalheiser Neil R NR

Database : the journal of biological databases and curation 20200101

Clinical case reports are the 'eyewitness reports' of medicine and provide a valuable, unique, albeit noisy and underutilized type of evidence. Generally, a case report has a single main finding that represents the reason for writing up the report in the first place. However, no one has previously created an automatic way of identifying main finding sentences in case reports. We previously created a manual corpus of main finding sentences extracted from the abstracts and full text of clinical ca ...[more]

PMID: 32525207

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Project description:ObjectivesTo compare the frequency of discrepancies in retracted reports of clinical trials with those in adjacent unretracted reports in the same journal.DesignBlinded case-control study.SettingJournals in PubMed.Population50 manuscripts, classified on PubMed as retracted clinical trials, paired with 50 adjacent unretracted manuscripts from the same journals. Reports were randomly selected from PubMed in December 2012, with no restriction on publication date. Controls were the preceding unretracted clinical trial published in the same journal. All traces of retraction were removed. Three scientists, blinded to the retraction status of individual reports, reviewed all 100 trial reports for discrepancies. Discrepancies were pooled and cross checked before being counted into prespecified categories. Only then was the retraction status unblinded for analysis.Main outcome measureTotal number of discrepancies (defined as mathematically or logically contradictory statements) in each clinical trial report.ResultsOf 479 discrepancies found in the 100 trial reports, 348 were in the 50 retracted reports and 131 in the 50 unretracted reports. On average, individual retracted reports had a greater number of discrepancies than unretracted reports (median 4 (interquartile range 2-8.75) v 0 (0-5); P<0.001). Papers with a discrepancy were significantly more likely to be retracted than those without a discrepancy (odds ratio 5.7 (95% confidence interval 2.2 to 14.5); P<0.001). In particular, three types of discrepancy arose significantly more frequently in retracted than unretracted reports: factual discrepancies (P=0.002), arithmetical errors (P=0.01), and missed P values (P=0.02). Results from a retrospective analysis indicated that citations and journal impact factor were unlikely to affect the result.ConclusionsDiscrepancies in published trial reports should no longer be assumed to be unimportant. Scientists, blinded to retraction status and with no specialist skill in the field, identify significantly more discrepancies in retracted than unretracted reports of clinical trials. Discrepancies could be an early and accessible signal of unreliability in clinical trial reports.

Dataset Information

Identifying main finding sentences in clinical case reports.

Publications

Identifying main finding sentences in clinical case reports.

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