Injury outcome indicators: the development of a validation tool: Table 1
Bibliographic record
Abstract
BACKGROUND: Researchers have previously expressed concern about some national indicators of injury incidence and have argued that indicators should be validated before their introduction. AIMS: To develop a tool to assess the validity of indicators of injury incidence and to carry out initial testing of the tool to explore consistency on application. METHODS: Previously proposed criteria were shared for comment with members of the International Collaborative Effort on Injury Statistics (ICE) Injury Indicators Group over a period of six months. Immediately after, at a meeting of Injury ICE in Washington, DC in April 2001, revised criteria were agreed over two days of meetings. The criteria were applied, by three raters, to six non-fatal indicators that underpin the national road safety targets for Canada, New Zealand, and the United Kingdom. Consistency of ratings were judged. CONSENSUS OUTCOME: The development process resulted in a validation tool that comprised criteria relating to: (1) case definition, (2) a focus on serious injury, (3) unbiased case ascertainment, (4) source data for the indicator being representative of the target population, (5) availability of data to generate the indicator, and (6) the existence of a full written specification for the indicator. On application of these criteria to the six road safety indicators, some problems of agreement between raters were identified. CONCLUSION: This paper has presented an early step in the development of a tool for validating injury indicators, as well as some directions that can be taken in its further development.
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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 | 0.080 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".