Complications: acknowledging, managing, and coping with human error
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.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".