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. ClÃnica 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 Womenâs Hospital, Boston, MA, USA; 10Albert Einstein College of Medicine, Bronx, NY, USA; 11Instituto Nacional de CancerologÃa - INCAN, Ciudad de México, MéxicoCorrespondence: Rodrigo Diez-Tafur, Pain Management Unit. ClÃnica 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.055 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".