Uncertainty and objectivity in clinical decision making: a clinical case in emergency medicine
Bibliographic record
Abstract
The evidence-based practice and evidence-based medicine (EBM) movements have promoted standardization through guideline development methodologies based on systematic reviews and meta-analyses of best available research. EBM has challenged clinicians to question their reliance on practical reasoning and clinical judgement. In this paper, we argue that the protagonists of EBM position their mission as reducing uncertainty through the use of standardized methods for knowledge evaluation and use. With this drive towards uniformity, standardization and control comes a suspicion towards intuition, creativity and uncertainty as integral parts of medical practice. We question the appropriateness of attempts to standardize professional practice through a discussion of the importance of uncertainty. Greenhalgh's taxonomy of uncertainty is used to inform an analysis of the clinical reasoning occurring in a potentially life threatening emergency situation with a young patient. The case analysis is further developed by the use of the Canadian philosopher Bernard Lonergan's theory about understanding and objective knowing. According to Lonergan it is not by getting rid of or even by reducing uncertainty, but by attending systematically to it and by relating to it in a self-conscious way, that objective knowledge can be obtained. The paper concludes that uncertainty is not a regrettable and unavoidable aspect of decision making but a productive component of clinical reasoning.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".