Socio-economic variation in CT scanning in Northern England, 1990-2002
Bibliographic record
Abstract
BACKGROUND: Socio-economic status is known to influence health throughout life. In childhood, studies have shown increased injury rates in more deprived settings. Socio-economic status may therefore be related to rates of certain medical procedures, such as computed tomography (CT) scans. This study aimed to assess socio-economic variation among young people having CT scans in Northern England between 1990 and 2002 inclusive. METHODS: Electronic data were obtained from Radiology Information Systems of all nine National Health Service hospital Trusts in the region. CT scan data, 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 census data, using χ2 tests and Spearman rank correlations. RESULTS: During the study period, 39,676 scans were recorded on 21,089 patients, with 38,007 scans and 19,485 patients (11344 male and 8132 female) linkable to Townsend scores. The overall distributions of both scans and patients by quintile of Townsend deprivation scores were significantly different to the distributions of Townsend scores from the census wards included in the study (p < 0.0001). There was a significant association between type of scan and deprivation quintile (p < 0.0001), primarily due to the higher proportions of head scans in the three most deprived quintiles, and slightly higher proportions of chest scans and abdomen and pelvis scans in the least deprived groups. There was also a significant association (p < 0.0001) between the patient's age at the time of the CT scan and Townsend deprivation quintiles, with slightly increasing proportions of younger children with increasing deprivation. A similar association with age (p < 0.0001) was seen when restricting the data to include only the first scan of each patient. The number of scans per patient was also associated with Townsend deprivation quintiles (p = 0.014). CONCLUSIONS: Social inequalities exist in the numbers of young people undergoing CT scans with those from deprived areas more likely to do so. This may 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".