Arrogance of ‘but all you need is a good index finger’: A narrative ethics exploration of lack of universal funding of PSA screening in Canada
Bibliographic record
Abstract
This narrative ethics exploration stems from my happy prostate-specific antigen (PSA) story, though it should not have been, as I annually refuse my family physician's recommendation to purchase PSA screening. The reason for my refusal is I teach ethics to medical students and of course must walk the talk, and PSA screening is not publicly funded in the province of Ontario, Canada. In addition, I might have taken false comfort in 'but all you need is a good index finger' to detect prostate cancer, expounded by a senior physician at a national medical conference in 2010, and applauded by the large audience of physicians. I was compelled to begin this exploration out of survivor guilt, although I will not be a survivor for long, and as a mea culpa to the men similarly situated to me in having late diagnosis of prostate cancer, aggressive tumours and multiple metastases, but who unlike me are dead because they did not experience the physician-educator-based exceptionisms and coincidences that permit me to still be alive. Although my PSA story will always be a happy story, even when my life ends in a few years, the initiation of public funding of PSA screening for all men over 50 would make my PSA story an even happier story.
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.059 | 0.052 |
| Scholarly communication | 0.019 | 0.007 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 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".