The Road to Improving Patient-Reported Outcomes: Measures or Healthcare Reform?
Bibliographic record
Abstract
Some argue that the way to improve the current health system is to ask patients about their experiences and perceptions of whether specific interventions (e.g., surgery) achieve expected health outcomes. Others argue that the way to improve health outcomes is to reform the system, particularly for those patients who suffer from complex chronic diseases and symptoms that do not fall neatly into a clinical pathway. I argue that patient reported outcome measures based on our current healthcare delivery system are necessary but not sufficient to improve patient outcomes. Ongoing dialogue, leadership and action are urgently needed to achieve a preferred future where our silo/sector/disciplinary based health system is reformed into an integrated person-centred 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 | 0.115 | 0.272 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.011 | 0.032 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.061 | 0.076 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".