Bibliographic record
Abstract
As I began writing this article, I was stunned realizing that September 2019 marks the anniversary of a ten-year journey for the specialty of emergency medicine (EM) in the United Arab Emirates (UAE). I had returned home to the UAE after 17 years’ acquiring and refining knowledge and skills as well as building experience and expertise abroad. This included medical school studies in Ireland,1 an Emergency Medicine (EM) Residency training in Montreal, Quebec2, a Prehospital Care fellowship in Toronto, Ontario,3 a Disaster Medicine fellowship in Boston, Massachusetts4, and finally a public health graduate degree in Baltimore, Maryland5. Throughout that time spent in nations where EM was well-developed, I was persistently asking myself, “What can I learn from here to allow me to develop EM back home?”. This challenging journey was certainly exciting and beneficial and exposed me to so many different “systems”, to their strengths and weaknesses, to the different approaches used to address problems, needs and day-to-day operations, and reinforced my belief that there is room and a need for flexibility, variability and diversity in the EM models one could build.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.109 | 0.046 |
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".