Using routinely recorded ethnicity: analysis of waiting times for elective admissions by ethnic group
Bibliographic record
Abstract
OBJECTIVE: To assess whether patients from non-white ethnic groups wait longer than white patients for elective in-patient admissions at St Mary's Hospital in London. METHODS: Patients who came off the waiting list for an elective inpatient admission between 1 April 2000 and 31 March 2001 were selected. A multivariable log linear model was developed to assess geometric mean waiting times for Black, Asian, Other and Missing ethnic groups compared to the White group, adjusted for age, sex, urgency and distance. RESULTS: Caution is needed in interpreting results, as a large number of patients had no usable ethnic code. There was no strong evidence that waiting times for ethnic groups were systematically different than for the White group. However, there was some evidence that white patients waited longer for a coronary arteriography than patients in other ethnic groups. This was partially explained by age, sex, clinical urgency and residential distance from St Mary's. CONCLUSIONS: The large proportion of patients with no usable ethnic code, lack of robust methods for case-mix adjustment and multiple ethnic categories makes analysis methodologically difficult. Regular and informative analysis of ethnic coded data is a necessary step in improving the accuracy and completeness of coding.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".