Expediting medical literature coding with query‐building
Bibliographic record
Abstract
Abstract Manual sorting of published journal articles into several pre‐defined subsets for the purpose of qualitative analysis is common practice in social science research. Unfortunately, this can be a time‐consuming process which requires the attention of a subject specialist, and relies on various measures of inter‐rater reliability to ensure that the results are valid and reproducible to serve as a basis for further study. We describe a system we have implemented, steelir, to help determine features common to one set of PubMed® articles in order to distinguish them from another. The system provides users with word‐level unigram and bigram features from the article title and abstract, as well as MeSH® indexing terms, and suggests robust sample queries to find similar articles. We apply the system to the task of distinguishing original research articles on functional magnetic resonance imaging (fMRI) of sensorimotor function from fMRI studies of higher cognitive functions.
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 | 0.048 | 0.172 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.032 | 0.019 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".