Bibliographic record
Abstract
The practice of clinical medicine involves not only the science of universal laws applied toward an intervention in disease but also the art of attending to someone’s particular circumstances of suffering. However, while the scientific method is rigorous, the art of attending suffering remains to be formalized. I develop an approach to the art of attending suffering in three stages. First, I outline the kinds of movements that Thomas Aquinas describes in the sensory nature: apprehension of, appetition toward, and finally abiding in the good. Second, I offer a descriptive definition of suffering as a hindrance to these sensory movements in pursuit of the good. Finally, I submit that practical reason is that intelligence which enables the sensory nature to move toward flourishing while emerging from suffering. If the sensory nature is “hard-wired” toward the good while avoiding the not-so-good, and if the work of practical reason is to pursue with prudence the good and shun evil, then it becomes apparent that the sensory nature and practical reason are both engaged in the movements of emerging from suffering-as-evil into flourishing-as-good. I conclude that while the scientific method guides our medical interventions, it is the dynamics of the sensory nature, rendered intelligent by practical reason, that are important for the art of attending suffering. It is practical reason with its habit of prudence that affords the medical art of attending the sufferer its formal aspect.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.109 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".