Patient-Centred Care: The Proving Ground for Continuity and Equity in Our Health System.
Bibliographic record
Abstract
In contrast to usual practice in Healthcare Papers, we will start this editorial with a personal story. Several years ago, one of our parents (ADB’s mother) was diagnosed with her fourth cancer, at a late stage and with little question about the eventual outcome. Her son was still at a relatively early stage in his career and at an even earlier stage in his personal life, having just gotten married. So she decided that she would play for time. She talked with her physicians and surgeons about her preference for quantity of time over quality of life, and they agreed on an aggressive course of treatment. However unpleasant the side effects of treatment were, she would have rated her experience as overwhelmingly positive because her goals drove treatment decisions. After a short while, the physician responsible for her care moved to another job and her new physician recommended palliative care and did not recommend further treatment. We cannot comment on the clinical reasoning behind this decision, but we can share how the story ended. She switched physicians, completed several more unpleasant rounds of chemotherapy and got to see several milestones, including – for her most importantly – the birth of her two grandchildren. Several years later, she did enter palliative care, where again her goals drove treatment plans and she was able to die at home as she wished. In this one, highly personal case, we can see three examples of strong patient engagement with a good patient experience and one Patient-Centred Care: The Proving Ground for Continuity and Equity in Our Health System
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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 | 0.034 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.026 | 0.029 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.014 | 0.029 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".