Mapping the Science of palliative care: A bibliometric analysis of the top 100 cited articles
Bibliographic record
Abstract
BACKGROUND: The palliative care evidence base has grown substantially in recent years with the benefits, barriers, and facilitators of care delivery well established across many settings and contexts. AIM: We aimed to rigorously and systematically delineate the trends, themes, and scope of the top 100 papers aided by bibliometrics to map the field of palliative care science and identify future directions for the field. DESIGN: We conducted a bibliometric analysis in accordance with the BIBLIO checklist for reporting the bibliometric reviews. DATA SOURCE: Employing a comprehensive search string we examined the Scopus online database from inception to December 14th, 2024, to identify and retrieve pertinent publications. Extracted data included year of publication, number of citations and other metrics, authorship, and study design, among others. RESULTS: Total citations for the 100 most cited articles ranged from 5083 to 419. Most articles originated from the US (43%), United Kingdom (16%), and Canada (15%). Overall, 83 different first authors and 87 senior authors contributed; about half first authors and 32% of senior authors were women. Forty-two different journals published the articles. Key themes were end-of-life care, palliative care integration within different medical sub-specialties (e.g. oncology, respiratory disease), clinical tool development and validation, and symptom management. CONCLUSION: Our findings provide a comprehensive map of the palliative care scientific landscape with key implications for future research, clinical practice, and policy. These results can be used to mitigate scientific disparities in author representation, ensure appropriate evidence use across international contexts, and empower high-quality evidence-based palliative care advocacy.
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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 | 0.023 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.307 | 0.326 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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, 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".