Prostate cancer: socio‐economic, geographical and private‐health insurance effects on care and survival
Bibliographic record
Abstract
OBJECTIVE: To examine the effects of demographic, geographical and socio-economic factors, and the influence of private health insurance, on patterns of prostate cancer care and 3-year survival in Western Australia (WA). PATIENTS AND METHODS: The WA Record Linkage Project was used to extract all hospital morbidity, cancer and death records of men diagnosed with prostate cancer between 1982 and 2001. The likelihood of having a radical prostatectomy (RP) was estimated using logistic regression, and the likelihood of death 3 years after diagnosis was estimated using Cox regression. RESULTS: The proportion of men undergoing RP increased six-fold, from 3.1% to 20.1%, over the 20 years, whilst non-radical surgery (transurethral, open or closed prostatectomy) simultaneously halved to 29%. Men who had RP were typically younger, married and with less comorbidity. Patients with a first admission to a rural hospital were much less likely to have RP (odds ratio 0.15; 95% confidence interval, CI, 0.11-0.21), whereas residence alone in a rural area had less effect (0.54, 0.29-1.03). A first admission to a private hospital increased the likelihood of having RP (2.40, 2.11-2.72), as did having private health insurance (1.77, 1.56-2.00); being more socio-economically disadvantaged reduced RP (0.63, 0.47-0.83). The 3-year mortality rate was greater with a first admission to a rural hospital (relative risk 1.22; 95% CI 1.09-1.36) and in more socio-economically disadvantaged groups (1.34, 1.10-1.64), whereas those admitted to a private hospital (0.77, 0.71-0.84) or with private health insurance (0.82, 0.76-0.89) fared better. Men who had RP had better survival than those who had non-radical surgery (4.85, 3.52-6.68) or no surgery (6.42, 4.65-8.84), although this may be an artefact of a screening effect. CONCLUSION: The 3-year survival was poorer and the use of RP less frequent in men from socio-economically and geographically disadvantaged backgrounds, particularly those admitted to rural or public hospitals, and those with no private health insurance.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".