CHLA 2023 Conference Contributed Papers/Congrès de l'ABSC 2023: Communications Libres
Bibliographic record
Abstract
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 assessmentGary Atwood University of Vermont 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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.480 | 0.372 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".