Top‐Cited Articles in Problem‐Based Learning: A Bibliometric Analysis and Quality of Evidence Assessment
Bibliographic record
Abstract
The aims of this study were to identify characteristics of the top-cited articles in problem-based learning (PBL) and assess the quality of evidence provided by these articles. The most frequently cited articles on PBL were searched in April 2015 in the Science Citation Index Expanded database (List A) and Google Scholar database (List B). Eligible articles identified were reviewed for key characteristics. The Oxford Centre for Evidence-Based Medicine guidelines were used in assessing the level of evidence. The number of citations varied (62 to 923 on List A and 218 to 2,859 on List B). Countries that contributed the majority of articles in both lists were the United States, Netherlands, United Kingdom, and Canada. No significant correlations were found between number of citations and number of years since published (p=0.451), number of authors (p=0.144), females in authorship (p=0.189), non-medical authors (p=0.869), number of institutions (p=0.452), and number of grants (p=0.143), but a strong correlation was found with number of countries involved (p=0.007). Application of the Oxford hierarchy of evidence showed that 36 articles were at levels 4 and 5 of evidence. This study found that research articles represented approximately one-third of PBL articles assessed and reported mainly on questionnaire-based studies. The most highly cited articles occupied top-ranking positions in the journals in which they were published. The lower level of evidence observed in most top-cited articles may reflect the significance of innovative ideas or content of these articles. These findings have implications for dental educators and dental researchers.
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 | BibliometricsMetaresearch Domain: Evaluation · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | 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 | 0.052 | 0.261 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.209 | 0.170 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".