Bibliographic record
Abstract
Problem-solving is an essential human endeavor. It involves problem definition, followed by solution design, solution implementation, and, finally, review. Healthcare is a prime and often successful example of problem-solving. In medical terminology, the problem definition is termed the diagnosis, the solution design is called the treatment plan, and the solution itself is the provision of treatment. The case review is used to assess the efficacy of the solution. This process is sequential and consequential. Obviously, if a problem's definition is absent or incorrect, then an appropriate solution cannot be designed or implemented. Unfortunately, missed or incorrect diagnoses are not uncommon in healthcare and can cause considerable harm and economic cost. Minimizing these diagnostic errors would confer great benefits to patients, clinicians, and healthcare organizations. Understanding the nature of diagnosis and why it can be so difficult are some of the first steps in reducing these diagnostic errors. One issue is that diagnosis may seem to be more of an art than a science when the exact mechanisms of diagnosis are often not clearly described. Clinicians typically learn their diagnostic craft by mimicry of their mentors over years of hard experience. This absence of a clear method not only makes diagnosis difficult to do well it also makes diagnosis difficult to teach well. The purpose of this paper is to try to better articulate the fundamental principles and methods of diagnosis and to make diagnosis more intellectually understandable, and so more teachable, and more achievable by clinicians.
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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".