An Analysis of the Bibliometrics of the Mexican Institute in Ophthalmology: A Case Study of an Emerging Research-Based Educational Health Institute
Bibliographic record
Abstract
The Instituto Mexicano de Oftalmología (IMO), a non-profit eye institute, has experienced significant growth. In this case study, we analyze IMO-affiliated publications (2012-2023) and introduce the Degree of Involvement in paper authorship index (DI-index). Journal metrics were extracted from InCites Journal Citation Reports and Scimago. IMO research output grew from one publication (2012) to 31 (2023), peaking at 45 (2018). The average impact factor rose from 0.2 (2012) to 5.2 (2022). The DI-index totaled 106.6 across 227 articles. Bibliometric analysis provides valuable insights for emerging institutions, and the DI-index provides a novel approach to evaluating authorship involvement.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.030 | 0.060 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".