How to Think in Medicine: Reasoning, Decision Making, and Communication in Health Sciences and Professions
Bibliographic record
Abstract
Mastery of quality health care and patient safety begins as soon as we open the hospital doors for the first time and start acquiring practical experience. The acquisition of such experience includes much more than the development of sensorimotor skills and basic knowledge of sciences. It relies on effective reason, decision making, and communication shared by all health professionals, including physicians, nurses, dentists, pharmacists, and administrators. How to Think in Medicine, Reasoning, Decision Making, and Communications in Health Sciences is about these essential skills. It describes how physicians and health professionals reason, make decision, and practice medicine. Covering the basic considerations related to clinical and caregiver reasoning, it lays out a roadmap to help those new to health care as well as seasoned veterans overcome the complexities of working for the well-being of those who trust us with their physical and mental health. This book provides a step-by-step breakdown of the reasoning process for clinical work and clinical care. It examines both the general and medical ways of thinking, reasoning, argumentation, fact finding, and using evidence. It explores the principles of formal logic as applied to clinical problems and the use of evidence in logical reasoning. In addition to outline the fundamentals of decision making, it integrates coverage of clinical reasoning risk assessment, diagnosis, treatment, and prognosis in evidence-based medicine. Presented in four sections, this book discusses the history and position of the problem and the challenge of medical thinking; provides the philosophy interfacing topics of interest for health sciences professionals including the probabilities, uncertainties, risks, and other quantifications in health by steps of clinical work; decision making in clinical and community health care, research, and practice; Communication in clinical and community care including how to write medical articles, clinical case studies and case reporting, and oral and written communication in clinical and community practice and care.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".