Inter-rater agreement in defining chemical incidents at the National Poisons Information Service, London
Bibliographic record
Abstract
BACKGROUND: National surveillance for chemical incidents is being developed in the UK. It is important to improve the quality of information collected, standardise techniques, and train personnel. OBJECTIVE: To define the extent to which eight National Poison Information Service specialists in poison information agree on the classification of calls received as "chemical incidents" based on the national definition. DESIGN: Blinded, inter-rater reliability measured using the kappa statistic for multiple raters. SETTING: National Poison Information Service and Chemical Incident Response Service, Guy's and St Thomas's NHS Trust, London. PARTICIPANTS: Eight specialists in poison information who are trained and experienced in handling poisons information calls and have been involved in extracting information for surveillance. RESULTS: The overall level of agreement observed was at least 69% greater than expected by chance (kappa statistic). Fire and incidents where chemicals were released within a property had a very good level of agreement with kappa statistic of 83% and 80% respectively. The lowest level of agreement was observed when no one or only one person was exposed to a chemical (33%) and when the chemical was released into the air (48%). CONCLUSION: High levels of agreement were observed. There is a need for more training and improvement in consistency of the data collected by all organisations.
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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 | 0.178 | 0.230 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".