Introduction of a New Toxicology Consult Service in a Large Tertiary Care Teaching Hospital.
Bibliographic record
Abstract
BACKGROUND: Clinical toxicology is not a certified specialty in Israel, consequently there are a limited number of toxicologists and toxicology services available for consultation. OBJECTIVES: To establish a medical toxicology consultation service focusing on bedside consultations, which had not previously been available in Israel. METHODS: This single-center, retrospective chart review of toxicology consults was conducted during the first years after the initiation of a new toxicology service. RESULTS: From September 2017 to December 2021, 1703 toxicology consultations were conducted. The most common exposures and reasons for consultation included psychotropic medications (427, 23%), analgesics and anti-inflammatory medications (353, 19%), household products (312, 17%), substances of abuse (240, 13%), and natural toxins (142, 8%). Bedside medical toxicology consultations were performed in 1036 cases (62%) during daytime and night shifts. The number of consultation requests increased steadily over the study period. CONCLUSIONS: The new toxicology service led to a significant change in the institution's approach to toxicological patients. A bedside toxicology service could help reduce the healthcare burden on national poison centers and can offer readily available, personalized, medical toxicology care.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".