Classification and Consequences of Errors in Otolaryngology
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.
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 | 0.006 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".