PubMed’s Native Interface Remains the Best Tool for Systematic Searching of its Biomedical Citations
Bibliographic record
Abstract
A Review of: Wildgaard, L. E., & Lund, H. (2016). Advancing PubMed? A comparison of third-party PubMed/Medline tools. Library Hi Tech, 34 (4), 669-684. http://dx.doi.org/doi: 10.1108/LHT-06-2016-0066 Abstract Objective – To compare the functionality of third-party PubMed tools for searching biomedical citations in PubMed, in the specific context of systematic searching. Design – Comparative analysis of software functionality. Setting – Online, freely accessible search software. Subjects – Sixteen third-party tools for searching and managing the full range of PubMed citations (tools which focused on specific disciplines were not included). Methods – Tools for analysis were identified in two ways; those discussed in two published articles were used, and a supplementary PubMed search was performed. The initial list of 76 possibilities was assessed for study inclusion on 4 criteria: covering the entire range of PubMed content; being freely available; not limiting to a particular bio-medical discipline; and incorporating online PubMed/MEDLINE content. After assessment, 16 tools were chosen for further analysis (the authors provide a list and description of the tools in their Table I). Each was examined in relation to 11 crucial operational aspects. Result sets were tested against a control (a literature search result set on a particular clinical question which was determined by physicians to yield relevant results, details of which are provided by the authors in an online appendix). Main Results – The 11 identified aspects related to tool functionality were examined for each tool selected, with results grouped into three sets of factors: 1) supporting the search (field codes, filters, limits and Boolean operators); 2) managing the search (output, related articles, links to articles, number of results, exporting); and 3) documenting the search (saving the search and search history). In some cases, the tests had to be adjusted to accommodate the tool's specifications. In Table II the authors present a grid with the results of the testing, on each of the 11 aspects, for each tool. The authors found that with many tools it was not straightforward, if even possible, to filter and limit in order to get more specific result sets. Few tools were effective at suggesting related articles within the tool itself, instead linking the user out to PubMed, and only two tools provided the same number of citation results as the comparison PubMed search. In addition, the display of results often made it difficult to assess result sets; and only two tools provided the option to save searches and see search history. Furthermore, due to unexpected tool limitations, it was not possible to assess the relevance of citation result sets delivered by the third-party tools, as compared with the control PubMed search. Conclusion – Close analysis of the tools studied indicated that they were not created in order to support systematic searches. They lack support for filtering/limiting, saving or exporting searches, which are central functionalities to the work of performing such searches. While some of the tools studied may still be in the early phases of development, and while several of them, in enhancing PubMed searches in particular ways, may suggest additional profitable strategies for performing a systematic search, not one of them can replace the functionalities of the native PubMed interface. It remains the best tool for searching and managing the full range of PubMed citations, for the purposes of performing systematic searches.
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 | 0.141 | 0.399 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.005 |
| Bibliometrics | 0.121 | 0.102 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.179 | 0.084 |
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".