Addressing the Future of Pain Medicine Training: Redevelopment of Post-Doctoral Training as an Even More Imperative Standard in Latin America
Bibliographic record
Abstract
Rodrigo Diez-Tafur,1,2 Victor M Silva-Ortiz,3 Carlos Guerrero-Nope,4 Juan Felipe Vargas-Silva,5 Camila Lobo,6 Fabricio Dias Assis,6 Michael E Schatman,7,8 Christopher L Robinson,9 Sudhir Diwan,10 Ricardo Plancarte-Sanchez11 1Pain Management Unit. Clínica Anglo americana, Lima, Perú; 2Centro MDRS: Sports, Spine & Pain Centers, Lima, Perú; 3Pain Unit, Hospital Zambrano Hellion. Monterrey, Nuevo León, México; 4Pain Management Unit. Hospital Fundación Santa Fe, Bogotá, Colombia; 5Interventional Pain Management Unit. Hospital Pablo Tolón Uribe, Medellin, Colombia; 6Singulair Pain Management Center. Campinas, Sao Paulo, Brasil; 7Department of Anesthesiology, Perioperative Care and Pain Medicine, NYU Grossman School of Medicine, New York, NY, USA; 8Department of Population Health - Division of Medical Ethics, NYU Grossman School of Medicine, New York, NY, USA; 9Department of Anesthesiology, Perioperative, and Pain Medicine, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA, USA; 10Albert Einstein College of Medicine, Bronx, NY, USA; 11Instituto Nacional de Cancerología - INCAN, Ciudad de México, MéxicoCorrespondence: Rodrigo Diez-Tafur, Pain Management Unit. Clínica Anglo americana, Avenida Emilio Cavenecia 251 of 101. Miraflores, Lima, 15073, Perú, Tel +51 937010418, Email rodrigo.dieztafur@mail.mcgill.ca
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 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".