For Non-expert Clinical Searches, Google Scholar Results are Older with Higher Impact while PubMed Results Offer More Breadth
Bibliographic record
Abstract
Objectives – To compare PubMed and Google Scholar results for content relevance and article quality Design – Bibliometric study. Setting – Department of Internal Medicine at Texas Tech University Health Sciences Center. Methods – Four clinical searches were conducted in both PubMed and Google Scholar. Search methods were described as “real world” (p. 216) behaviour, with the searchers familiar with content, though not expert at retrieval techniques. The first 20 results from each search were evaluated for relevance to the initial question, as well as for quality. Relevance was determined based on one author’s subjective assessment of information in the title and abstract, when available, and then tested by two other authors, with discrepancies discussed and resolved. Items were assigned to one of three categories: relevant, possibly relevant, and not relevant to the question, with reviewer agreement measured using a weighted kappa statistic. The quality of items found to be ‘relevant’ and ‘possibly relevant’ was measured by impact factor ratings from Thomsen Reuters (ISI) Web of Knowledge, when available, as well as information obtained by SCOPUS on the number of times items were cited. Main Results – Google Scholar results were judged to be more relevant and of higher quality than results obtained from PubMEed. Google Scholar results are also older on average, while PubMed retrieved items from a larger number of unique journals. Conclusion – In agreement with earlier research, the authors recommended that searchers use both PubMed and Google Scholar to improve on the quality and relevance of results. Searches in the two resources identify unique items based upon the ranking algorithms involved.
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 | 0.121 | 0.499 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.071 | 0.070 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 0.015 |
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".