Classification and Consequences of Errors in Otolaryngology
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: To develop a preliminary classification system for errors in otolaryngology. METHODS: A retrospective, anonymous survey was distributed to 2,500 members of the American Academy of Otolaryngology-Head and Neck Surgery (AAO-HNS). Respondents were asked whether an error had occurred in their practice in the last 6 months, and if so, to describe the error, its consequences, and any corrective action taken. RESULTS: There were 466 (18.6%) responses. Two hundred ten (45% of respondents) otolaryngologists reported 216 errors. A classification system for errors in otolaryngology was developed. Errors were classified as related to history and physical (1.4%), differential or final diagnosis (1.4%), testing (10.4%), surgical planning (9.9%), wrong-site surgery (6.1%), anesthesia-related (3.3%), wrong drug/dilution on the surgical field (3.8%), technical (19.3%), retained foreign body (0.9%), equipment-related (9.4%), postoperative care (8.5%), medical management (13.7%), nursing/ancillary (0.5%), administrative (6.6%), communication (3.8%), and miscellaneous (0.9%). There were 78 cases of major morbidity and 9 deaths. If these data are representative, there may be more than 2,600 episodes of major morbidity and more than 165 deaths related to medical error in otolaryngology patients annually. CONCLUSIONS: Human error in otolaryngology occurs in all practice components, including diagnostic, treatment, surgical, communication, and administrative. Types of errors reported by otolaryngologists differ from those reported by other specialists. Error classification systems may need to reflect each specialty's realm of practice. Errors in otolaryngology cause appreciable morbidity and mortality. Quantitative study of errors and the development of targeted prevention and amelioration strategies should be a high priority.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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