Complications: acknowledging, managing, and coping with human error
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Errors are inherent in medicine due to the imperfectness of human nature. Health care providers may have a difficult time accepting their fallibility, acknowledging mistakes, and disclosing errors. Fear of litigation, shame, blame, and concern about reputation are just some of the barriers preventing physicians from being more candid with their patients, despite the supporting body of evidence that patients cite poor communication and lack of transparency as primary drivers to file a lawsuit in the wake of a medical complication. Proper error disclosure includes a timely explanation of what happened, who was involved, why the error occurred, and how it will be prevented in the future. Medical mistakes afford the opportunity for individuals and institutions to be candid about their weaknesses while improving patient care processes. When a physician takes the Hippocratic Oath they take on a tremendous sense of responsibility for the care of their patients, and often bear the burden of their mistakes in isolation. Physicians may struggle with guilt, shame, and a crisis of confidence, which may thwart efforts to identify areas for improvement that can lead to meaningful change. Coping strategies for providers include discussing the event with others, seeking professional counseling, and implementing quality improvement projects. Physicians and health care organizations need to find adaptive ways to deal with complications that will benefit patients, providers, and their institutions.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 it