New Search Strategies Successfully Optimize Retrieval of Clinically Sound Treatment Studies in EMBASE. A review of: Wong, Sharon S‐L, Nancy L. Wilczynski, and R. Brian Haynes. “Developing Optimal Search Strategies for Detecting Clinically Sound Treatment Studies in EMBASE.” Journal of the Medical Library Association 94.1 (Jan. 2006): 41‐47. 14 May 2007 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1324770.
Notice bibliographique
Résumé
<b>Objective</b> – To develop and test the sensitivity and specificity, precision andaccuracy of search strategies to retrieve clinically sound treatment studies in the EMBASE database.<br><b>Design</b> – Analytical study.<br><b>Setting</b> – Methodologically sound studies of treatment from 55 journals indexed in EMBASE for the year 2000.<br><b>Subjects</b> – EMBASE and hand searches performed at the Health Information Research Unit of McMaster University, Ontario, Canada.<br><b>Methods</b> – The authors compare the results of EMBASE searches using their search strategies with the “gold standard” of articles retrieved by hand search. Research assistants initially hand searched each issue of 55 selected journals published in 2000 to identify articles detailing studies on healthcare treatment of humans. Subject coverage of the journals was wide ranging and included obstetrics and gynaecology, psychiatry, oncology, neurology, surgery and general practice. Studies were then assessed to ensure they met the qualifying criteria: random allocation of participants to groups, outcome assessment of at least 80% of participants who began the study, and analysis consistent with study design. Initially, 3850 articles on treatment were identified, of which 1256 (32.6%) were methodologically sound. To construct a comprehensive set of search terms, input was sought from librarians and researchers in the US and Canada. This initially produced a list of 5385 terms, of which 4843 were unique and 3524 produced hits. Individual search terms with sensitivity greater then 25% and specificity greater then 75% were incorporated into search strategies for use within the OVID interface for the EMBASE database to retrieve articles meeting the same criteria. These strategies were developed using all 27,769 articles published in the 55 journals in 2000. This all inclusive approach was used to test the search strategies’ ability to identify high quality treatment articles from a larger pool of material.<br><b>Main results</b> – The single term which achieved best sensitivity was “random:mp,”with a sensitivity of 95.1%. This same term achieved a high specificity of 92.5%. The best‐performing single term for specificity was “randomized:tw” at 96.7%, but this did reduce sensitivity to 63.2%. The single term to achieve the best balance between the two was “clinical trial:mp,” with a sensitivity of 88.3% and specificity of 88.0%. Combining terms produced varied results, and Table 3 within the article details terms used to give the best combinations for sensitivity, specificity and optimisation of both. The best three‐term search strategies for sensitivity achieved a rate just shy of 99% with a specificity of 72.0%, while the optimum three‐term strategy for specificity achieved 96.7% but with a trade off of lowering the rate of sensitivity to 51.7%. The best‐performing combination of search terms to optimise sensitivity and specificity produced values exceeding 92% for both.<br><b>Conclusion</b> – The authors present search strategies which can successfully be used to retrieve methodologically sound studies on the prevention and treatment of disease and health complications indexed on the EMBASE database. A clear outline of the trade‐off between sensitivity and specificity of the strategies is included.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,018 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,001 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,006 |
| Science ouverte | 0,004 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».