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Enregistrement W1573641917 · doi:10.18438/b87g61

Searching for Adverse Effects in MEDLINE and EMBASE Requires a Combined Approach for Efficient Retrieval

2006· article· en· W1573641917 sur OpenAlexvenueno aff
Marcy L. Brown

Notice bibliographique

RevueEvidence Based Library and Information Practice · 2006
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueAcademic integrity and plagiarism
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMEDLINEAdverse effectCochrane LibraryInformation retrievalMedicineControlled vocabularyComputer scienceMeta-analysisPharmacologyInternal medicine

Résumé

récupéré en direct d'OpenAlex

A review of: Golder, Su, Heather M. McIntosh, Steve Duffy, and Julie Glanville. “Developing Efficient Search Strategies to Identify Reports of Adverse Effects in MEDLINE and EMBASE.” Health Information & Libraries Journal 23.1 (Mar. 2006): 3-12. Objective – To assess the sensitivity and precision of various search strategies for retrieving adverse effects studies from the MEDLINE and EMBASE databases. Design – Analytical survey. Subjects – A case study using a recently published systematic review of the effectiveness and adverse effects of seven new anti-epileptic drugs. Setting – MEDLINE and EMBASE searches performed by researchers at the Centre for Reviews and Dissemination and the UK Cochrane Centre Search Filters Design Group at the University of York, UK. Methods – Five key approaches to searching were defined. The first approach used either text words or controlled vocabulary to search for specific adverse effects. The second used subheadings or qualifiers either attached to drug names found in the controlled vocabulary (approach 2a) or ‘floating’ without drug names (approach 2b). The third approach used text words as synonyms for the phrase ‘adverse effects.’ The fourth used controlled indexing terms for adverse effects. The fifth and final approach used two published search strategies incorporating study design (Badgett et al., Loke et al.). These five approaches were used to search for studies of the adverse effects of seven new anti-epileptic drugs. 5,011 unique papers were retrieved. Of these, 236 were judged potentially relevant and 225 full text articles were obtained. The inclusion criteria from a previously published systematic review (Wilby et al.) were applied to the papers, and 79 met the criteria. Five papers were added to the set after being identified from reference lists, clinical experts, and other sources. This new set of 84 studies was used as a quasi gold standard (QGS) against which more than 300 combinations of the five approaches could be tested. To create the set of possible approaches, the researchers combined search strategies one through four in all possible ways, and used all available subheading combinations from 2a and 2b. The Badgett and Loke searches were tested separately. Main Results – Sensitivity and precision were determined for each combination. Formulas used to calculate sensitivity and precision were provided. In MEDLINE, search strategies using floating subheadings achieved the highest sensitivity. The most useful single subheading in both MEDLINE and EMBASE was “adverse effects,” with 79.1% and 79.5% sensitivity respectively. Of the more than 300 combinations tested, the most sensitive combination in MEDLINE included specified adverse effects in combination with the floating subheadings “adverse effects,” “complications,” and “drug effects,” together with text words for adverse effects. This strategy had 97.0% sensitivity, but low precision at 2.8%. The highest precision was achieved by using subheadings attached to drug indexing terms. In EMBASE, the strategy of Loke et al. provided the highest sensitivity at 86.3% and precision of 2.0%. Since researchers are not likely to know in advance all of the reported adverse effects of a particular drug therapy, the most sensitive strategies without specific adverse events were also identified. The search with the highest sensitivity in MEDLINE had 95.5% sensitivity, and 97.3% sensitivity in EMBASE. Conclusion – Searching for adverse effects requires a combination of approaches in both MEDLINE and EMBASE. In MEDLINE, the most sensitive combination yielded 97.0% sensitivity. Regardless of the approach used, precision remains low. An effective generic search filter for adverse effects searches may not yet be feasible. More research is needed on search strategies, as well as more consistent methods of reporting and indexing adverse effects.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,090
score de la tête « metaresearch » (Gemma)0,310
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: Méthodes · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,910
Score d'incertitude au seuil0,476

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0900,310
Méta-épidémiologie (sens strict)0,0030,003
Méta-épidémiologie (sens large)0,0150,007
Bibliométrie0,0820,077
Études des sciences et des technologies0,0020,004
Communication savante0,0090,013
Science ouverte0,0050,008
Intégrité de la recherche0,0040,004
Charge utile insuffisante (le modèle a refusé de juger)0,1060,034

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,016
Tête enseignante GPT0,295
Écart entre enseignants0,280 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeObservationnel
DomaineMéthodes
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2006
Routes d'admission1
Résumé présentoui

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