Bibliographic record
Abstract
It is our privilege to be invited to write the editorial for this special edition of the Colombian Journal of Anesthesiology (CJA) and shed some light on the importance of the campaign and its impact in the anesthesiology practice.The core principles of Choosing Wisely International (Decisiones Acertadas in Colombia) align very well with the global WHO/PAHO strategy of "integrated people-centred health services (IPCS)" as it spurs conversations between patients and their health providers.This global movement was launched in the United States by the ABIM foundation in 2012 with the aim to improve healthcare quality and reduce unnecessary treatments and tests.The foundation of the campaign rests on physician societies creating list of tests or treatments in their discipline for which there is excellent scientific evidence of overuse or harm to patients.Today, the campaign is active in over 25 countries around the world.In Latin America, the campaign was adopted by Brazil in 2015 and was recently endorsed by its first Spanish speaking country, Colombia, in May 2022, thanks to the leadership of Dr. Ramon Abel Castaño and Dr.
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 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.098 | 0.078 |
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".