Stefan Friedrichsdorf Oral History.
Bibliographic record
Abstract
Dr. Stefan Friedrichsdorf begins the interview by describing his time as medical trainee in Germany and his observations that the medical treatments physicians were giving seriously ill children were often the cause of the distressing pain symptoms children experienced. In his pursuit to find out more about managing pain in children, he found there were no existing resources -- no books, no courses, no practices around him. In a chance opportunity, one of his peers was awarded funding to conduct an assessment of pediatric palliative care in Germany, and so Dr. Friedrichsdorf became the manager of that study and began his lifelong mission to eliminate medically caused pain and suffering in children. Dr. Friedrichsdorf then describes participating in an opportunity sponsored by the Open Society led by George Soros after the fall of East Germany, to participate in several international conferences with other people interested studying pediatric palliative care. This included other like-minded individuals from the United Kingdom, Australia, Albania, the United States, and Canada. Dr. Friedrichsdorf comments on the three different types of healthcare models in the world, how current services and realities were derived from the circumstances created by these healthcare models, and several shocking and egregious situations and practices that he experienced and found to be detrimental to the health of the patient children and their families. From the friendships he made during the beginning sessions of international pediatric palliative care conferences, Dr. Friedrichsdorf created a successful training collaborative and developed The Educational Palliative and End of life Care (EPEC) pediatrics training modules and certification resource for practitioners. With this training tool, he expects to continue to see the current and next generations of practitioners be well trained in alleviating pain and distressing symptoms in children. Dr. Friedrichsdorf concludes the interview by listing his plans to continue to build a future where all practitioners are trained in at least primary palliative care, increase the global access to pediatric palliative and pain care free of economic restrictions, implement a reframing of physician training to stop blindly pushing impractical intensive treatments when a palliative treatment would be more effective at reducing suffering and burden on children, and for the establishment of sustainable interdisciplinary palliative care teams in every children's hospital.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.436 | 0.203 |
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".