A comparison of the stages at which cancer is diagnosed in physicians and in the general population in Taiwan
Bibliographic record
Abstract
BACKGROUND: Previous investigations have reported that physicians tend to neglect their own health care; however, they may also use their professional knowledge and networks to engage in healthier lifestyles or seek prompt health services. We sought to determine whether the stage at which cancer is diagnosed differs between physicians and nonphysicians. METHODS: We conducted a nationwide matched cohort study over a period of 14 years in Taiwan. We accessed data from two national databases: the National Health Insurance Research Database and the Taiwan Cancer Registry File. We collected data on all patients with the 6 most common cancers in Taiwan (hepatoma, lung, colorectal, oral, female breast and cervical cancer) from 1999 to 2012. We excluded patients less than 25 years of age, as well as those with a history of organ transplantation, cancer or AIDS. We used propensity score matching for age, sex, residence and income to select members for the control (nonphysicians) and experimental (physicians) groups at a 5:1 ratio. We used χ(2) tests to analyze the distribution of incident cancer stages among physicians and nonphysicians. We compared these associations using multinomial logistic regression. We performed sensitivity analyses for subgroups of doctors and cancers. RESULTS: We identified 274,003 patients with cancer, 542 of whom were physicians. After propensity score matching, we assigned 536 physicians to the experimental group and 2680 nonphysicians to the control group. We found no significant differences in cancer stage distributions between physicians and controls. Multinomial logistic regression and sensitivity analyses showed similar cancer stages in most scenarios; however, physicians had 2.64-fold higher risk of having stage IV cancer at diagnosis in cases of female breast and cervical cancer. INTERPRETATION: In this cohort of physicians in Taiwan, cancer was not diagnosed at earlier stages than in nonphysicians, with the exception of stage IV cancer of the cervix and female breast.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".