Bibliographic record
Abstract
The first issue of "Investigación Clinica" was published in July, 1960; by the initiative of Dr. Américo Negrette. The journal had a small format (1/16) and was very sober in its presentation. It consisted of about 45 pages, with four articles, that were mainly the product of conferences and seminars. As time went by, 45 years of existence, our journal has evolved following the continuous changes on international regulations; such as the 1/8 format, the impression on acid-free paper and its bibliography in compliance with the Vancouver rules. Currently, Investigaci6n Clinica appears in the best international biomedical indexes and publishes eight original articles on average, in about 100 pages per number. It gets to 106 libraries and health centers in Venezuela and 102 abroad. International peer-review of research articles represents 50% of the current scientific reviews solicited. It is opportune to thank all of those that have collaborated, in such disinterested way, with the journal over the years, and to extend our gratitude to the Editorial and Advisory Boards, the financial supporters, the publishing companies that have worked in its final printing and, of course, to all the researchers that have confided in the responsibility, seriousness and punctuality of Investigaci6n Clinica.
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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 0.025 |
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".