Prevalence of invasive cancer in a large general practice patient population in New Zealand
Bibliographic record
Abstract
INTRODUCTION The prevalence of cancer in the community is likely to be increasing due to an ageing population, implementation of cancer screening programmes and advances in cancer treatment. AIM To determine the prevalence of primary invasive cancers in a large general practice patient population in New Zealand and to characterise the health-care status of these cancer patients. METHODS Data were sourced from the patient management system of a large general practice (n=11,374 patients) in a medium-sized Waikato town and from the New Zealand Cancer Registry dataset to identify patients diagnosed with cancer between January 2009 and December 2018. RESULTS There were 206 cancer diagnoses in 201 patients; 35 cancers were diagnosed in 1887 Māori patients (1.9%) and 171 in 9487 non-Māori patients (1.8%). The age-standardised prevalence was 3092/100,000 in Māori patients and 1971/100,000 in non-Māori patients. The most prevalent cancers were breast, male genital organ, digestive organ and skin cancers. In May 2019, 81 of 201 (40.8%) patients with cancer were receiving only usual care from their general practitioner, whereas 66 (32.8%) were having their cancer managed in secondary care. Comorbidities were common, including hypertension (38.8%), gastrointestinal disorders (29.9%) and mood disorders (24.4%). DISCUSSION Results suggest that there may be disparities in cancer prevalence between Māori and non-Māori patients, although this needs to be confirmed in other general practices. Furthermore, primary care appears to be responsible for most of the care in this patient cohort and workloads should be planned accordingly, particularly with the high incidence of comorbidities.
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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".