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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".