Fractures: A History and Philosophy of Patient Suffering in 20th-century American Medicine
Bibliographic record
Abstract
My dissertation explores the history and philosophy of patient suffering in 20th-century American medicine. Chapter One argues that historians of medicine colloquially synonymize suffering with related phenomena, such as pain, which risks treating suffering as a transhistorical object. That is a problem, first because suffering appears to be historically distinct, and second because neglecting it has undesirable consequences in the history of medicine and beyond. In response, Chapters Two and Three modestly enlarge the historical scholarship by presenting an intellectual and cultural history of American physician Eric Cassell’s (1928-2021) influential theory of suffering. This narrative argues that legal influences in Cassell’s early intellectual development and the medico-legal milieu in which he wrote provided the impetus, concepts, and language for his seminal theory. Chapter Four brings my historical findings to bear on current philosophical debates over Cassell’s view. Some critics argue that his account is too narrowly focused on damage, an objection I contextualize historically using the legal descriptions of suffering that influenced him by way of an explosion in medical malpractice lawsuits. My historical research thus lends credence to existing philosophical critiques. To further reinforce these critiques, I also introduce a case of suffering excluded by Cassell’s narrow account, which I call ‘paradoxical purposes.’ On the basis of this exclusion, I conclude that his view does not exhaust suffering as he intended. To rectify this shortcoming, Chapter Five amends his theory in two different ways. Both locate personal integrity, which Cassell says suffering affects, on a spectrum that ranges by ‘existential degrees.’ I refer to the lower end of this spectrum as ‘local suffering,’ which includes paradoxical purposes, whereas Cassell’s focus is on the higher end, ‘global suffering.’ Chapter Six explores two ways scholars can theorize about suffering along this spectrum. One exhausts suffering in general accounts, which I refer to as ‘monistic theories.’ The other involves a multiplicity of narrower models aimed at types of suffering, which I call ‘pluralistic theories.’ Next, I associate these theories with the conceptual questions to which they are most relevant in a bid to facilitate greater collaboration among theorists.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.039 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".