Sawubona reprise: reflections on the European Society of Thoracic Surgeons Presidential Address 2022
Bibliographic record
Abstract
On the 20 th June 2022 I had the privilege of a lifetime to deliver the Presidential Address at the 30 th ESTS Annual Meeting (1).Despite it took me nearly two years to conceive and prepare the talk, I never had any doubt on the choice of the topic because this represents very well the core of our profession: connecting with the suffering person in front of us and trying to help them navigating through their most vulnerable time in life.The following is the link to the recording of the talk: https://youtu.be/ZBE6CcSPxYM.I had the distinct privilege and pleasure of having many friends and esteemed colleagues in the Auditorium attending the lecture.I will be forever grateful to them for their enthusiastic response to contribute with their thoughts and reflections.I am convinced their words will highlight even more the importance of teaching and practicing empathy at all levels in our profession and life.
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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 | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.062 | 0.057 |
| Insufficient payload (model declined to judge) | 0.017 | 0.014 |
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".