An AOA critical issue. Medical errors in orthopaedics: practical pointers for prevention.
Bibliographic record
Abstract
The 1999 Institute of Medicine (IOM) report To Err is Human 1 focused the attention of the public and the media on adverse events occurring during the treatment of patients. Eye-catching newspaper headlines suggested that at least 44,000 and possibly high as 98,000 patients died yearly in the United States as a consequence of 1. However, even prior to publication of the IOM report, a number of professional medical associations, including the American Academy of Orthopaedic Surgeons (AAOS) and the Canadian Orthopaedic Association (COA), had recognized the importance of medical errors and had initiated programs to help physicians to foster a culture of patient safety. The IOM report did serve to heighten awareness of patient-safety issues in the minds of both patients and orthopaedic surgeons. Heretofore, prevention of medical errors had been considered a worthy, but cheerless matter deserving only limited time and resources in an era of ever-contracting medical finances. In the To Err is Human report, the IOM challenged professional medical organizations to make patient safety a priority item in their agendas, implored medical schools to include patient safety as part of their curricula, and urged regulatory agencies to monitor patient-safety data. In addition, patients were encouraged to be proactive in their own care and to be conscious of safety issues. In this new environment of awareness, the initiation of patient-safety programs has taken on a higher priority. Professional medical organizations such as the Canadian Orthopaedic Association and the American Academy of Orthopaedic Surgeons have been acknowledged for their foresight and willingness not only to take on but also to offer constructive solutions to a difficult and unpopular problem. The Canadian Orthopaedic Association, the American Orthopaedic Association, and the American Academy of Orthopaedic Surgeons have embraced a commitment to patient safety for a number …
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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 | 0.007 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.032 | 0.021 |
| Insufficient payload (model declined to judge) | 0.068 | 0.035 |
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".