Algorithmic indexing in MEDLINE frequently overlooks important concepts and may compromise literature search results
Bibliographic record
Abstract
Objective: To evaluate the appropriateness of indexing of algorithmically-indexed MEDLINE records. Methods: We assessed the conceptual appropriateness of Medical Subject Headings (MeSH) used to index a sample of MEDLINE records from February and March 2023. Indexing was performed by the Medical Text Indexer-Auto (MTIA) algorithm. The primary outcome measure is the number of records for which the MTIA algorithm assigned subject headings that represented the main concepts of the publication. Results: Fifty-three percent of screened records had indexing that represented the main concepts discussed in the article; 47% had inadequacies in the indexing which could impact their retrieval. Three main issues with algorithmically-indexed records were identified: 1) inappropriate MeSH assigned due to acronyms, evocative language, exclusions of populations, or related records; 2) concepts represented by more general MeSH while a more precise MeSH is available; and 3) a significant concept not represented in the indexing at all. We also noted records with inappropriate combinations of headings and subheadings, even when the headings and subheadings on their own were appropriate. Conclusions: The indexing performed by the February-March 2023 calibration of the MTIA algorithm, as well as older calibrations, frequently applied irrelevant or imprecise terms to publications while neglecting to apply relevant terms. As a consequence, relevant publications may be omitted from search results and irrelevant ones may be retrieved. Evaluations and revisions of indexing algorithms should strive to ensure that relevant, accurate and precise MeSH terms are applied to MEDLINE records.
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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 | 0.271 | 0.668 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.033 | 0.032 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".