Bibliographic record
Abstract
One quiet night in the autumn of 2016, I had a dream that I was ill, very ill. I had no recollection of what led up to my illness or what affected me, but there I was, weak, tired, almost lifeless as gravity pressed against my extremities. Through the silence, bouts of rhythmic ticking arose from the depths of my chest while familiar faces flashed one-by-one before my eyes. I cycled through moments of fear, disbelief, grief, anger, and at times, fleeting hope that this was all just a senseless, transient dream. Finally, as darkness gave way to the morning light, I found myself back in the usual calmness of my bedroom. The sun was shining; the air was gentle; and my heart, steady as ever, was still beating. In my painting When Dreams End, on the cover of this issue, I reflect on my dream, an end-of-life experience that came shortly after a patient I cared for passed away from metastatic cancer during my third year of medical school. My patient was diagnosed only weeks before with the glorious years of early midlife just ahead of her, and she struggled both physically and emotionally amid her rapid decompensation in the hospital. In our brief time together, I often imagined myself in her situation, but I could only imagine. The foreground of my painting features my patient’s hands with a hospital bracelet around her wrist, marking her transition from health to illness. Her hands carefully shield a dandelion from the wind, paralleling the vulnerability of life, especially at its final stages, and its resistance against an undesirable fate. Inevitably, the dandelion greets the gentle winds that scatter its seed away. With the years that have elapsed, my patient’s name, face, and details of her story have become hazy. However, I still remember the feeling of being present at the peak of a patient’s suffering for the first time, hoping our days together had, in some way, been therapeutic. While my dream may not represent the end-of-life process for every patient, as a then future physician who would come to have regular encounters with death and dying, it was a poignant reminder of the deeply human emotions that can arise when life expires. The experience was so individual and personal that no textbook or didactic teaching could truly impart what I learned. I hope that capturing this short-lived, but heartfelt memory in reflective images and words will inspire new understandings about our existence and enable deeper connections with patients at the final stages of their lives.When Dreams End
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.113 | 0.044 |
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".