Bibliometric Study of the Bachelor Theses on Oral Pathology Defended at the University of the Andes School of Dentistry, 2009-2019
Bibliographic record
Abstract
Oral or Buccal Pathology is a dental specialty based on Pathological Anatomy and Internal Medicine that studies the etiology, pathophysiological mechanisms and consequences of diseases that develop and manifest in the oral and maxillofacial region, being the basis for treatment and management of them. For this reason, it represents an area of importance in the university career, especially at the time of the presentation of the Undergraduate Degree Projects, which can be analyzed through bibliometrics. The study aimed to identify the behavior of the bibliometric indicators used for the undergraduate theses in the Stomatology area at the Faculty of Dentistry, University of The Andes (FOULA due to the acronym in Spanish) between the years 2009-2019. The research was descriptive with a documentary design. The analytical material was constituted by the FOULA Stomatology theses in digital format, from the Technical Council, and the database of the FOULA Research Department during the period 2009-2019. A total of 53 theses were conducted in the Stomatology area during that period, 4.81 papers per year, Vancouver citations were presented in 75.47% of the thesis; with an average of 52.98% references per thesis, it predominated the descriptive research type with 58.49% and transversal design with 60.78%; the prominent collection technique was observation with 18.86% and analysis of descriptive data with 64.15%.
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 | 0.008 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.076 | 0.122 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".