Evidence map›Paper›PMID 23272771›Full record

ArticleBMC medical research methodology2012

Search strategies to identify reports on "off-label" drug use in EMBASE.

Bita Mesgarpour, Markus Müller, Harald Herkner

Abstract read
In one paragraph

Article in BMC medical research methodology, 2012. 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. Article
  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.

Bita MesgarpourDepartment of Clinical Pharmacology, Medical University Vienna, General Hospital, Austria.
Markus Müller
Harald Herkner

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMedications are frequently prescribed outside their regulatory approval (off-label) by physicians particularly where appropriate therapies are not available. However, the risk/benefit ratio of drugs in off-label use needs to be critically appraised because it may differ from approved on-label usage. Therefore, an extensive exploration of current evidence on clinical data is well-advised. The objective of this study was to develop a search strategy that facilitates detection of the off-label drug use documents in EMBASE via OvidSP.

methodsWe constructed two sets of gold standards from relevant records to off-label drug use by a sensitive search of MEDLINE and EMBASE. Search queries, including search words and strings, were conceived based on definition of off-label use of medications as well as text analysis of 500 randomly selected relevant documents. The selected terms were searched in EMBASE (from 1988 to 2011) and their retrieval performance was compared with the gold standards. We developed a sensitivity-maximizing, and a sensitivity- and precision-maximizing search strategy.

resultsFrom 4067 records relevant to off-label drug use in our full gold standard set, 3846 records were retrievable from EMBASE. "off label*.af." was the most sensitive single term (overall sensitivity 77.5%, sensitivity within EMBASE 81.9%, precision 88.1%). The highest sensitive search strategy was achieved by combining 36 search queries with overall sensitivity of 94.0% and precision of 69.5%. An optimal sensitive and precise search strategy was yielded precision 87.4% at the expense of decreasing overall sensitivity to 89.4%.

conclusionWe developed highly sensitive search strategies to enhance the retrieval of studies on off-label drug use in OvidSP EMBASE.

Indexed as

Databases, BibliographicDrug LabelingOff-Label UseAlgorithmsDrug TherapyHumansInformation Storage and RetrievalMEDLINESearch Engine

Identifiers

PMID23272771
PMCPMC3543848

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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.