Producción científica sobre cuidados paliativos oncológicos en adultos mayores en Latinoamérica, en Scopus y en la Biblioteca Virtual en Salud
Bibliographic record
Abstract
Background: Palliative care (PC) aims to improve the quality of life, relieving both psychosocial, physical or spiritual suffering, the patient and their environment. Cancer occupies the second place among the diseases that require palliative care. However, the scientific contribution of Latin American countries amounts to only 2.1%, which is lower compared to world literature \nObjectives: To describe the scientific production of oncological palliative care in the elderly, in Latin America in Scopus and in the Biblioteca Virtual en Salud. \nMethods: An observational, descriptive, cross-sectional study was carried out, performing a bibliometric analysis, using the databases Scopus and Virtual Health Library, using previously selected Decs and Mesh, extracting the data: annual publication, country, journal, impact factor of the journal year of publication, authors, collaboration, institution, specialty, frequency of citation, type of study, employed population, type of cancer, type of palliative care. These data entered a database and then were analyzed with Stata 15.1 \nResults: A total of 106 original scientific articles were found. The publication trend has increased since 1998 - 2018, maximum peak 2014. The main language of publication was English 65 (60.37%). Brazil had the highest number of publications 42 (33%), followed by Mexico and Chile. The collaboration among Latin American countries 30 (28.57%), with other countries mostly, was Mexico with the United States, Canada, Australia. The main specialty of the authors was General Medicine (49%). The studies were mostly observational (89.6%), quantitative (85.5%), and the population most studied was patients (83.96%). The type of palliative care more frequently studied was quality of life and mental health (28.34%). Several types of cancer were studied simultaneously (80.19%). \nConclusion: While it is true that there has been an increase in publications in Latin American countries, the contribution to world literature is still well below other countries. Therefore, greater effort is needed to strengthen research capacity through the development of research networks and projects.
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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 | 0.021 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.121 | 0.154 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".