CHLA 2023 Conference Contributed Papers/Congrès de l'ABSC 2023: Communications Libres
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Notice bibliographique
Résumé
Introduction:The use of controlled vocabulary to identify relevant articles is a central element of bibliographic database instruction in health sciences.Students learning to search MEDLINE are taught that MeSH yields precise results, and that MeSH indexing increases an article's findability, reliably describing an article's contents.Indexing for MEDLINE was done completely by human indexers until 2011.Since April 2022, all articles are assigned MeSH via automated indexing (AI).Per the NLM, MeSH assigned by AI are determined based on terms in title, abstract, and terms and indexing of 'related records', with human review and curation of results "as appropriate".As MEDLINE instruction typically starts with teaching learners to identify key elements or concepts in their research question and find appropriate MeSH for them, we sought to explore the following: how well does AI identify key concepts of an article?Are concepts missed more or less when compared to human indexers?Drawing on the PICO framework, are missing concepts more often any particular PICO element?Methods: We reviewed samples of automated and human-indexed records from shortly before April 2022, and some entirelyautomated from later, to determine whether their main concepts were adequately represented with MeSH.Working in pairs, our team used a web form to assign key concepts (based on the PICO framework) that, per our experience, would be used to find it and similar articles based on title and abstract.Assigned MeSH were then displayed and analyzed to determine whether they adequately represented the key concepts of each record.Results & Conclusion: As the study is ongoing, results are forthcoming.Potential impacts of Automated Indexing on library instruction and basic searching will be discussed. CP2. Can GPT-3 tools accurately find and analyze articles for systematic reviews? A (very) preliminary assessment Gary Atwood University of VermontIntroduction: GPT-3 is a large language model that uses artificial intelligence to generate textual responses to prompts and questions.GPT-3 technology has been used to create several interesting tools including the widely reported chatbot ChatGPT-3, which was released in November 2022.Inspired by the initial success of GPT-3, several organizations have started to build tools designed to assist with tasks associated with the systematic review research process.This project will analyze how successful these tools are in completing two specific tasks: searching for research articles and analyzing individual articles.Methods/Description: This project consists of two parts.In part one, the research question from a previously published systematic review will be used to conduct a search in two GPT-3 based tools for relevant research articles.In part two, each GPT-3 tool will be used to analyze a single research article to determine if it is relevant to the research question Results/outcomes: For part one, the results will be based on how effective and efficient each tool is at finding relevant research articles.Results will be compared to the set of articles included in the original review as a measure of success.For part two, each tool will be used to pull evaluative information from the sample article.This information will be compared to a manual assessment completed by the author.Discussion: This project will provide researchers with guidance on how to integrate GPT-3 based tools into their systematic review workflow.It will include a brief discussion of strengths and weaknesses and how they can impact potential results. CP3. Preliminary results of a longitudinal study of health information-seeking behaviour preand post-COVID
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.
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 | 0,014 | 0,049 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 découle