The measurement of disability weights for 18 prevalent acute poisoning conditions
Bibliographic record
Abstract
BACKGROUND: Disability weights (DWs) are used in disease burden studies, with the calculation of the weight of the disability as years lived with disability versus years of lost life accounting for mortalities. Currently, there is a single DW score available for poisoning, which is considered to be a single health state. This makes it difficult to evaluate the differing burdens of poisonings involving various substances/conditions in comparison with other health states in countries with different patterns of substance abuse. The aim of this study is therefore to estimate the DWs of 18 common poisonings based on the expert elicitation method. METHODS: A panel of 10 medical clinicians who were familiar with the clinical aspects of different poisonings estimated the DWs of 50 health states by interpolating them on a calibrated Visual Analogue Scale. The DWs of some poisonings, such as alcohol, cannabis and heroin, had been estimated in previous studies and so were used to determine the external consistency of our panel. As a matter of routine, the DWs could vary on a scale between 0 (best health state) and 1 (worst health state). RESULTS: Statistical analysis showed that both the internal (Cronbach's α = 0.912) and external consistency of the panel were acceptable. The DWs for the different poisonings were estimated along a range from 0.830 for severe aluminium phosphide to 0.022 for mild benzodiazepine. CONCLUSIONS: Different poisonings should be weighted differently since they vary widely. Unfortunately, they are currently all weighted the same.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".