PubMed’s Native Interface Remains the Best Tool for Systematic Searching of its Biomedical Citations
Notice bibliographique
Résumé
A Review of: Wildgaard, L. E., & Lund, H. (2016). Advancing PubMed? A comparison of third-party PubMed/Medline tools. Library Hi Tech, 34 (4), 669-684. http://dx.doi.org/doi: 10.1108/LHT-06-2016-0066 Abstract Objective – To compare the functionality of third-party PubMed tools for searching biomedical citations in PubMed, in the specific context of systematic searching. Design – Comparative analysis of software functionality. Setting – Online, freely accessible search software. Subjects – Sixteen third-party tools for searching and managing the full range of PubMed citations (tools which focused on specific disciplines were not included). Methods – Tools for analysis were identified in two ways; those discussed in two published articles were used, and a supplementary PubMed search was performed. The initial list of 76 possibilities was assessed for study inclusion on 4 criteria: covering the entire range of PubMed content; being freely available; not limiting to a particular bio-medical discipline; and incorporating online PubMed/MEDLINE content. After assessment, 16 tools were chosen for further analysis (the authors provide a list and description of the tools in their Table I). Each was examined in relation to 11 crucial operational aspects. Result sets were tested against a control (a literature search result set on a particular clinical question which was determined by physicians to yield relevant results, details of which are provided by the authors in an online appendix). Main Results – The 11 identified aspects related to tool functionality were examined for each tool selected, with results grouped into three sets of factors: 1) supporting the search (field codes, filters, limits and Boolean operators); 2) managing the search (output, related articles, links to articles, number of results, exporting); and 3) documenting the search (saving the search and search history). In some cases, the tests had to be adjusted to accommodate the tool's specifications. In Table II the authors present a grid with the results of the testing, on each of the 11 aspects, for each tool. The authors found that with many tools it was not straightforward, if even possible, to filter and limit in order to get more specific result sets. Few tools were effective at suggesting related articles within the tool itself, instead linking the user out to PubMed, and only two tools provided the same number of citation results as the comparison PubMed search. In addition, the display of results often made it difficult to assess result sets; and only two tools provided the option to save searches and see search history. Furthermore, due to unexpected tool limitations, it was not possible to assess the relevance of citation result sets delivered by the third-party tools, as compared with the control PubMed search. Conclusion – Close analysis of the tools studied indicated that they were not created in order to support systematic searches. They lack support for filtering/limiting, saving or exporting searches, which are central functionalities to the work of performing such searches. While some of the tools studied may still be in the early phases of development, and while several of them, in enhancing PubMed searches in particular ways, may suggest additional profitable strategies for performing a systematic search, not one of them can replace the functionalities of the native PubMed interface. It remains the best tool for searching and managing the full range of PubMed citations, for the purposes of performing systematic searches.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,141 | 0,399 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,003 |
| Méta-épidémiologie (sens large) | 0,015 | 0,005 |
| Bibliométrie | 0,121 | 0,102 |
| Études des sciences et des technologies | 0,003 | 0,006 |
| Communication savante | 0,016 | 0,017 |
| Science ouverte | 0,007 | 0,013 |
| Intégrité de la recherche | 0,008 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,179 | 0,084 |
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 source (Gemma direct ou Codex distillé), 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 ».