[Road traffic accidents--severe injuries. Decision-making on the basis of partial data].
Bibliographic record
Abstract
BACKGROUND: Road traffic accidents are the cause of approximately one quarter of trauma hospitalizations in Israel. A comparison of figures on severe traffic injuries, as published by the Israeli Central Bureau of Statistics (CBS) and the Israeli Police with data from registries in medical systems, revealed significant disparities. AIMS: To present gaps between registries and the possible consequences that presenting incomplete data to decision makers may have on their ability to set policy for reducing road traffic accidents. RESULTS: The number of severe injuries according to the CBS, the National Council for Road Safety and the Israeli Police ranges from 3,378 to 2,573 per year, for the period 1998-2000. During the same time period, the national trauma registry that recorded data at only eight hospitals (of the 24 hospitals in the country), noted a total of 4,442 to 4,800 patients per year. The Ministry of Health's data, that includes figures from most of the hospitals in the country, reports between 10,290 to 11,009 road traffic accident hospitalizations per year for this same period of time. The CBS data is the formal national data, hence the database which decision makers use when considering the number of casualties due to road accidents consists of less than half of the actual number of cases. Furthermore, it is not a representative sample. When decisions are made without data, one uses common sense and reason. However, when the decision maker is presented with information, he assumes that these are valid, reliable, representative, well established data and relates to the information as such in the decision making process. If data is misleading, decisions may be ill-advised. SUMMARY AND CONCLUSION: Gaps in information are presented, posing a question on the possible effect that the interpretation of partial data by decision makers may have on the decisions they make. It is strongly advocated that collaboration is needed between police and health agencies and that a system for collecting and analyzing data on road traffic casualties be established to combine health and police data. The existence of a reliable, complete and valid database is essential in order to succeed in the important battle to reduce morbidity and mortality from road traffic accidents.
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".