P1-491 Socio-economic variation in the use of ct scans in young people in the North of England, 1990–2002
Bibliographic record
Abstract
Introduction Social patterning is known to influence health throughout life. In childhood, studies have shown increased injury rates in more deprived settings. Through this, it is also possible that socio-economic status may be related to rates of undergoing certain medical procedures with relatively high radiation doses, such as computed tomography (CT) scans. This study aimed to assess socio-economic variation among young people having CT scans in the North of England between 1990 and 2002. Methods Electronic data were obtained from Radiology Information Systems of all nine National Health Service hospital Trusts in the region. Data related to CT scans, including sex, date of scan, age at scan, number and type of scans were assessed in relation to quintiles of Townsend deprivation scores, obtained from linkage of postcodes with UK census data. Results During the study period, 39 676 scans were recorded on 21 089 patients. The number of scans and patients scanned differed in relation to quintiles of deprivation, with increasing numbers of scans and patients associated with increasing area-level deprivation. Significant associations were also seen between deprivation and age at scan, age at first scan, type of CT scan, and the number of scans per patient. Conclusion Social inequalities exist in the numbers of young people undergoing CT scans with those from deprived areas more likely to do so. This is likely to reflect the rates of injuries in these individuals and implies that certain groups within the population may receive higher radiation doses than others due to medical procedures.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".