Conciliating cognition and consciousness: the perceptual foundations of clinical reasoning
Bibliographic record
Abstract
Clinical reasoning has been defined as a form of cognition applied to evaluating and managing a patient's medical problem. As a kind of cognition, a product of the human psyche, it is logical to expect that clinical reasoning should be best understood through methods derived from psychology, neuropsychology and the cognitive sciences. However, the application of scientific methods to evaluating clinical reasoning is unable to analyse clinical reasoning in terms of first-person experience and consciousness. By reducing clinical reasoning to its cognitive components the cognitivist approach tends to ignore the larger context in which clinical reasoning occurs. By reducing its conception of clinical reasoning to its cognitive components, the neuropsychological approach fails to acknowledge clinical reasoning as a form of intentionality, a gestalt, grounded in human perception. A full epistemology of clinical reasoning requires a phenomenological analysis that can make sense of the relation between pre-reflective consciousness and explicit forms of knowing. In this paper I conciliate cognition and consciousness in medicine through analysing the phenomenology of perception in clinical reasoning. I compare the application of phenomenology to clinical reasoning with the attempt to model clinical reasoning on Aristotelian practical wisdom or phronesis. Finally, I analyse empathy as a type of perception critical for effective clinical interaction and exemplary for reflecting on perception as the intersubjective foundation of clinical reasoning.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.038 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".