Systematic follow‐up increases incidence of anaphylaxis during adverse reactions in anesthetized patients
Bibliographic record
Abstract
BACKGROUND: The incidence of hypersensitivity reactions during anesthesia is underestimated because clinical symptoms may vary and diagnosis is not obvious. Our aim was to investigate the consequences of a systematic follow-up of patients on the estimated incidence of allergic reactions during anesthesia. METHODS: We conducted a prospective study over a 2-year period (70,000 anesthesias). When patients were suspected with hypersensitivity reactions or with unexplained adverse reactions during anesthesia, blood was sampled to measure histamine and tryptase, and then skin tests were performed 4-6 weeks later. RESULTS: During the studied period, 39 patients were enrolled in the database. Eight were excluded because of lack of skin tests. Twenty-two patients had clinical features compatible with immediate hypersensitivity reaction, and nine had reactions rated as 'unexplained' by the attending physician. Following systematic investigation, we found 22 hypersensitivity reactions (15 patients with obvious and seven with unexplained reactions) during anesthesia. This increases the estimated incidence of hypersensitivity reactions from 1 : 4667 to 1 : 3180 anesthesias. Tryptase concentrations were increased in only 50% of these patients. In our series, positive and negative predictive values of tryptase at T(0) for the diagnosis of anaphylaxis were 100% and 60%, respectively. Latex was the major causative agent, followed by neuromuscular blocking agents and antibiotics. CONCLUSIONS: Systematic follow-up of patients with unexplained reactions during anesthesia increases the estimated incidence of IgE-mediated hypersensitivity reactions during anesthesia by 50%.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".