Using the PubMatrix Literature-Mining Resource to Accelerate Student-Centered Learning in a Veterinary Problem-Based Learning Curriculum
Bibliographic record
Abstract
Problem-based learning (PBL) creates an atmosphere in which veterinary students must take responsibility for their own education. Unlike a traditional curriculum where students receive discipline-specific information by attending formal lectures, PBL is designed to elicit self-directed, student-centered learning such that each student determines (1) what he/she does not know (learning issues), (2) what he/she needs to learn, (3) how he/she will learn it, and (4) what resources he/she will use. One of the biggest challenges facing students in a PBL curriculum is efficient time management while pursuing learning issues. Bioinformatics resources, such as the PubMatrix literature-mining tool, allow access to tremendous amounts of information almost instantaneously. To accelerate student-centered learning it is necessary to include resources that enhance the rate at which students can process biomedical information. Unlike using the PubMed interface directly, the PubMatrix tool enables users to automate queries, allowing up to 1,000 distinct PubMed queries to be executed per single PubMatrix submission. Users may submit multiple PubMatrix queries per session, resulting in the ability to execute tens of thousands of PubMed queries in a single day. The intuitively organized results, which remain accessible from PubMatrix user accounts, enable students to rapidly assimilate and process hundreds of thousands of individual publication records as they relate to the student's specific learning issues and query terms. Subsequently, students can explore substantially more of the biomedical publication landscape per learning issue and spend a greater fraction of their time actively engaged in resolving their learning issues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.084 | 0.050 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".