Bibliographic record
Abstract
Following two months of backpacking in Southeast Asia, I arrived in Nepal in August 2014 for a much anticipated three-week global health elective in emergency medicine. Before I finished my first day, however, my trip took an unexpected turn; I began to experience a serious medical problem and was forced to seek immediate treatment. I was suddenly transformed from an enthusiastic student to a reluctant patient in a country whose medical system is very different from that of my own. This unfortunate circumstance did, however, allow me to learn about Nepalese medicine in ways that I never would have been able to as a medical student, and the lessons that I learned will undoubtedly help me in my future career. Suite à deux mois de voyage en Asie du Sud-Est, je suis arrivé au Népal en août 2014 où j’ai fait un stage de trois semaines en médecine d’urgence. Suite à ma première journée de stage, j’ai dû me chercher un traitement médical pour un problème sérieux. J’ai été transformé d’étudiant enthousiaste en patient inquiet dans un pays où le système médical est très différent du mien. Mon expérience comme patient et étudiant en médecine au Népal m’a permis d’apprendre beaucoup au sujet de la médecine népalaise. Les leçons apprises dans ce pays étranger vont sans doute aider dans ma future carrière en tant que médecin.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".