So many databases, such little clarity: Searching the literature for the topic aboriginal.
Bibliographic record
Abstract
OBJECTIVE: To describe the scope, content, and organization of commonly used medical databases and search strategies, using a search of the topic aboriginal to illustrate the various ways the topic is covered in each of the databases. DESIGN: Comparison of literature searches. METHOD: Seven common medical databases were searched using all the MeSH terms that are permutations of aboriginal. A secondary analysis using the "remove duplicates" function in Ovid was done to identify articles specific to each database. MAIN OUTCOME MEASURES: Number of articles found by each search. RESULTS: Searching by MeSH terms often produces very different information from that found when searching by text word. A unique term, such as Ojibway, is best found with a text word search. A more general term, such as Aborigines, is best searched by subject using a MeSH term. Many databases can be searched through Ovid and might all use different MeSH terms for the same reference. PubMed default searches that use MeSH terms and text words simultaneously often produce very large numbers of articles. In searching for North American aboriginal using MeSH terms, MEDLINE and PubMed produced the most references, followed by Healthstar. Calculating distinct "all aboriginal" references in EMBASE, Healthstar, and PsycINFO indicated that MEDLINE produced nearly all the articles found in Healthstar. In fact, MEDLINE alone produced 88% of the articles found in MEDLINE and EMBASE and 79% of the articles found in MEDLINE and PsycINFO. CONCLUSION: Although several researchers and medical librarians have noted that MEDLINE and EMBASE are quite distinct databases, suggesting both need to be searched for a complete search, we did not find that to be the case for the topic aboriginal. The results of this study demonstrate that using MEDLINE produces the most extensive coverage of literature on the topic aboriginal. To fully capture the complete body of available literature on other subjects might require searches of many databases, depending on the topic.
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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 | 0.031 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.022 | 0.033 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".