Bibliographic record
Abstract
I am a palliative care physician for 30 years. And I have been teaching " Lessons of Life " to medical students and elementary, junior high, and high school students for 30 years. Based on the words left behind by the deceased patient, these are classes to think about life through death. I would like to introduce some of the lectures at this conference. When I took care of a 23-year-old female terminal cancer patient, her pain of bone metastasis, which could not be removed, was relieved by a wedding ceremony. I was taught that pain is relieved not by drugs but by supporting the hopes and dreams of patients. A 21-year-old woman with cancer of unknown primary cancer, who had not been told of her prognosis, realized that she was dying and left a letter for her mother. She wrote, "I am glad I was born as your daughter” with gratitude. A 17-year-old high school male student, who had a brain tumor, left a diary. In the diary, he wrote, "If I were to die tomorrow, what would I do today? All I can do now is to live my life to death as I am.”An 18-year-old woman, battling rhabdomyosarcoma,said,“Walking, talking, seeing, hearing, laughing, crying, and living. You may think it’s normal as someone who always takes it for granted, but that’s not the case.” Through the words and actions left behind by my patients, I learn that we are living a day that is irreplaceable.
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 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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.034 | 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".