Health‐insurance status is a determinant of the stage at presentation and of cancer control in European men treated with radical prostatectomy for clinically localized prostate cancer
Bibliographic record
Abstract
OBJECTIVE: To determine whether health-insurance status might result in more localized stage at presentation, more favourable stage at surgery and in a lower rate of biochemical recurrence (BCR), in patients diagnosed with prostate cancer and treated with radical prostatectomy (RP), as despite uninhibited access to healthcare, private and public health insurance are available in most European countries. PATIENTS AND METHODS: In all, 4442 consecutive men had RP in two large European centres, of whom 2372 had public and 2070 had private health insurance. The groups were compared for several variables according to insurance status (private vs public). Means and proportions tests were complemented with logistic regression or Kaplan-Meier analyses. RESULTS: Serum prostate-specific antigen level (P < 0.001), clinical stage (P < 0.001), pathological Gleason sum (P = 0.02), positive surgical margin rate (18.4% vs 25.4%, P < 0.001), extracapsular extension rate (17.7% vs 20.0%, P = 0.047) and seminal vesicle invasion rate (9.6% vs 11.6%, P = 0.04) were more favourable in privately insured patients. Conversely, the rate of lymph-node involvement was higher in those with private than public insurance (4.4% vs 3.3%, P = 0.045). In univariate analyses addressing pathological variables, private insurance was invariably protective (all P < 0.05). The Kaplan-Meier analyses showed that privately insured patients had a lower rate of BCR after RP (log-rank P = 0.017). CONCLUSION: Despite uninhibited access to healthcare, insurance status represents a rate-limiting variable, which affects stage at presentation and the outcome of cancer control.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".