Preliminary guideline for reporting bibliometric reviews of the biomedical literature (BIBLIO): a minimum requirements
Bibliographic record
Abstract
BACKGROUND: A bibliometric review of the biomedical literature could be essential in synthesizing evidence if thoroughly conducted and documented. Although very similar to review papers in nature, it slightly differs in synthesizing the data when it comes to providing a pile of evidence from different studies into a single document. This paper provides a preliminary guideline for reporting bibliometric reviews of the biomedical literature (BIBLIO). METHODS: The BIBLIO was developed through two major processes: literature review and the consensus process. The BIBLIO started with a comprehensive review of publications on the conduct and reporting of bibliometric studies. The databases searched included PubMed, Scopus, Web of Sciences, and Cochrane Library. The process followed the general recommendations of the EQUATOR Network on how to develop a reporting guideline, of which one fundamental part is a consensus process. A panel of experts was invited to identify additional items and was asked to choose preferred options or suggest another item that should be included in the checklist. Finally, the checklist was completed based on the comments and responses of the panel members in four rounds. RESULTS: The BIBLIO includes 20 items as follows: title (2 items), abstract (1 item), introduction/background (2 items), methods (7 items), results (4 items), discussion (4 items). These should be described as a minimum requirements in reporting a bibliometric review. CONCLUSIONS: The BIBLIO for the first time provides a preliminary guideline of its own kind. It is hoped that it could contribute to the transparent reporting of bibliometric reviews. The quality and utility of BIBILO remain to be investigated further.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Reporting · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | BibliometricsMetaresearch Domain: Reporting · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
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 | 0.536 | 0.783 |
| Meta-epidemiology (narrow) | 0.006 | 0.011 |
| Meta-epidemiology (broad) | 0.014 | 0.021 |
| Bibliometrics | 0.051 | 0.050 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.025 | 0.020 |
| Open science | 0.019 | 0.019 |
| Research integrity | 0.027 | 0.029 |
| Insufficient payload (model declined to judge) | 0.011 | 0.019 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".