Bibliographic record
Abstract
Clinicians must celebrate and study medical errors. The dark culture of blame must be replaced by a scholarly culture of safety. This commentary presents six cases that show what we can learn from errors. The first step to identifying and understanding patient safety problems is to develop a common language for discussing patient safety. Latent unsafe conditions are ongoing circumstances of daily practice that reduce the safety of patients. An error is the failure ofa planned action to be completed as intended (error of execution), or the use of a wrong plan to achieve an aim (error of planning). Errors can be intercepted by appropriate action that minimizes the threat to patient safety. An adverse event is any unintended result of medical treatment that results in prolonged hospital stay, morbidity or mortality. If an adverse event is caused by an error, or series of errors, then it is a preventable adverse event. The teaching hospital is the first place where students (physicians, nurses, pharmacists and all other disciplines) are exposed to the culture of healthcare. It is essential to expose students to a culture of safety early in their training. Clinicians can make safety an academically important activity. Clinicians will find it difficult to undertake major safety initiatives given the existing constraints on time and energy. Although clinicians can identify the safety problems,there must also be a commitment to understand safety problems and make improvements. It is strongly recommended that hospitals train, implement and support Patient Safety Consultation Teams.
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 | 0.042 | 0.138 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.028 |
| Scholarly communication | 0.021 | 0.051 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.030 | 0.067 |
| Insufficient payload (model declined to judge) | 0.021 | 0.014 |
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".