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Record W1505933869 · doi:10.18438/b8r32c

Translation of Hedges in Medical Databases to Other Platforms’ Syntax May Cause Significantly Different Search Results

2011· article· en· W1505933869 on OpenAlexaffvenue
Heather Ganshorn

Bibliographic record

VenueEvidence Based Library and Information Practice · 2011
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsUniversity of Calgary
FundersU.S. National Library of Medicine
KeywordsMEDLINENational libraryCochrane LibraryComputer scienceMedicineInformation retrievalDatabaseLibrary science

Abstract

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Objective – To determine whether the methodological search filters in OvidSP MEDLINE and OvidSP EMBASE also known as Clinical Queries hedges had been modified from the originals which were written by the McMaster University Health Information Research Unit Hedges Group (the Haynes Group) and whether the translations of these hedges by the National Library of Medicine used in PubMed and EBSCO MEDLINE were reliable. The hedges examined are for the clinical categories of diagnosis, therapy, etiology, prognosis, clinical prediction guides, and reviews. The author also examined the translated National Library of Medicine (NLM) Systematic Reviews hedges in OvidSP MEDLINE and EBSCO MEDLINE. Design – Validity of hedges used in various databases. Setting – OvidSP MEDLINE, OvidSP EMBASE, EBSCO MEDLINE and PubMed were studied. Subjects – The Clinical Queries hedges designed to facilitate enhanced retrieval of particular types of studies in the above-mentioned databases were compared. Methods – The author ran the Clinical Queries hedges in OvidSP MEDLINE, OvidSP EMBASE and PubMed. Next, she manually entered the original Haynes Group published hedge search strings for each clinical query in these databases, and compared the results to the Clinical Queries. The author also compared the results obtained from the Ovid MEDLINE Clinical Queries versus the hedges in PubMed and EBSCO MEDLINE. The percentage difference in number of hits between the Ovid platform and the other platform was calculated. Where the difference was greater than 10%, the author modified the search string and re-tested it. There was no gold standard for comparison, so it was not possible to make calculations such as sensitivity, specificity, precision, or accuracy. For the testing of the Review hedges, the author used the Cochrane Database of Systematic Reviews as a gold standard to compare search results. She also compared the results in OvidSP MEDLINE to the results in EBSCO MEDLINE and PubMed. Main Results – Comparing the 27 OvidSP Clinical Queries limits to the equivalent Haynes search strings, the author found identical results, suggesting that the OvidSP hedges have not been changed from Haynes’ original search strings. However, when the OvidSP MEDLINE hedges were compared to PubMed and EBSCO, there were discrepancies. If the hedges were translated exactly, one should expect the result sets to be nearly identical, with the exception of records that had not yet been uploaded to OvidSP and EBSCO (PubMed contains records that are not yet fully indexed). However, other problems became evident. While the majority of searches yielded similar numbers of records, there were discrepancies of >10% in the number of hits for five of the Clinical Queries. Some of the hedges involved truncated search terms that, in PubMed, generated a message indicating that only the first 600 variations of the word root would be used. The author modified these hedges in order to obtain potentially more accurate results, though as she does not have a gold standard set for comparison, the modified hedges could not be thoroughly evaluated. Three of the EBSCO MEDLINE Clinical Queries hedges also generated significantly different results from OvidSP MEDLINE. The author was able to modify these hedges to generate similar results to those found in PubMed. The author’s examination of the various systematic review hedges identified other problems. For these hedges, it was possible to use the Cochrane Database of Systematic Reviews as a simple gold standard to assess the reliability of these filters. The Haynes Clinical Queries Review hedge is used in OvidSP EMBASE. The author found that this hedge’s sensitive filter retrieved 100% of the Cochrane Reviews, while the optimized filter retrieved all reviews but one. However, the specific filter retrieved only 16% of the Cochrane reviews. The author notes that the Haynes hedges were developed using a subset of journals that did not include the Cochrane Database of Systematic Reviews. The Clinical Queries Review hedge in MEDLINE appeared to have better results. In OvidSP, the sensitive and optimized hedges found all but one record, while the specific hedge found 83% of the records, a result that was mirrored in EBSCO MEDLINE and PubMed. Conclusion - Users of OvidSP MEDLINE can be confident that the Clinical Queries limits are true translations of the hedges published by Haynes et al., as they were found to give identical results to manual entry of these hedges. However, users cannot be confident that these queries will give the same results in PubMed, due to differences in syntax between the two interfaces. Users of EBSCO MEDLINE can be less confident that the Clinical Queries have been perfectly translated from the original Haynes queries, as three of these queries were found to yield significantly different results from the OvidSP MEDLINE search. The author recommends that OvidSP be the search interface of choice when using these hedges in MEDLINE. The National Library of Medicine’s (NLM) Systematic Reviews hedge has been translated into OvidSP and EBSCO, but has never been validated. The author found significant errors in this hedge in the OvidSP version, which were rectified after she contacted Ovid. However, Ovid was reluctant to share its translation of the hedge, as this is proprietary information. The author recommends that for this reason, it is best to use PubMed to search for systematic reviews, as the search string for its hedge is publicly available. The author also notes that this issue of proprietary information is very problematic for librarians, as it makes it impossible for them to assess the hedges they are using from vendors, or to identify the source of the problem when they get unusual results.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.126
metaresearch head score (Gemma)0.460
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.874
Threshold uncertainty score0.665

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1260.460
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0040.006
Bibliometrics0.0280.032
Science and technology studies0.0010.002
Scholarly communication0.0070.007
Open science0.0020.005
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0270.006

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.657
GPT teacher head0.485
Teacher spread0.173 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainMethods
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2011
Admission routes2
Has abstractyes

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