Embracing the Certainty of Uncertainty: Implications for Health Care and Research
Bibliographic record
Abstract
"Uncertainty" is the ongoing realization that we cannot predict the future, and "surprise" reminds us lest we forget. Despite the fact that uncertainty is an undeniable fact of everyday experiences, in particular when providing care to patients, it is ignored and under-evaluated scientifically. Understandably and appropriately, medical science seeks knowledge, certainty, and prediction; however, the fundamental truth of intrinsic irreducible uncertainty remains neglected. The principal hypothesis of this article is that greater acceptance and understanding of intrinsic uncertainty offers valuable insights towards improving the delivery and management of health care, as well as the performance of clinical and basic science research. This review highlights the ubiquitous presence and acceptance of irreducible uncertainty in diverse domains of science, defines and classifies uncertainty arising from this awareness, and explores the insights and implications of this understanding with regard to health-care practice, health-care management, physician-patient communication, basic science research, and clinical research. It offers specific recommendations in each area of focus that are proposed to stimulate deliberation and investigation.
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 | 0.160 | 0.192 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.008 | 0.089 |
| Scholarly communication | 0.028 | 0.047 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.020 | 0.024 |
| Insufficient payload (model declined to judge) | 0.007 | 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".