Miller Fisher’s Rules and Digital Health: The Best of Both Worlds
Bibliographic record
Abstract
BACKGROUND: Professor Fisher's legacy, defined by meticulous observation, curiosity, and profound knowledge, has established a foundational cornerstone in medical practice. However, the advent of automated algorithms and artificial intelligence (AI) in medicine raises questions about the applicability of Fisher's principles in this era. Our objective was to propose adaptations to these enduring rules, addressing the challenges and leveraging the opportunities presented by digital health. SUMMARY: The adapted rules we propose advocate for the harmonious integration of traditional bedside manners with contemporary technological advancements. The judicious use of advanced devices for patient examination, recording, and sharing, while upholding patient confidentiality, is pivotal in modern practice and academic research. Additionally, the strategic employment of AI tools at the bedside, to aid in diagnosis and hypothesis generation, underscores their role as valued complements to clinical reasoning. These adapted rules emphasize the importance of continual learning from experience, literature, and colleagues, and stress the necessity for a critical approach toward AI-derived information, which further consolidates clinical skills. These aspects underscore the perpetual relevance of Professor Fisher's rules, advocating not for their replacement but for their evolution. Thus, a balanced methodology that adeptly utilizes the strengths of AI and digital tools, while steadfastly maintaining the core humanistic values, arises as essential in the modern practice of medicine. KEY MESSAGES: A commitment between traditional medical wisdom and modern technological capabilities may enhance medical practice and patient care. This represents the future of medicine - a resolute commitment to progress and technology, while preserving the essence of medical humanities.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".