Using patient-reported outcome measures to assess health-care quality
Bibliographic record
Abstract
The transparency of surgical outcomes data and the drive for quality has been highlighted since the public inquiry, led by Professor Ian Kennedy, into children's heart surgery at the Bristol Royal Infirmary. This was formalized in Lord Darzi's 2008 report High Quality Care for All, that proposed the NHS should: 'systematically measure and publish information about the quality of care'. Subsequently the NHS White paper, Equity and Excellence: Liberating the NHS (Department of Health, 2010), set out the ambitions and aims of the NHS and in particular that it should provide: '...a service that offers care that is safe and of the highest quality.'
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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 | 0.089 | 0.336 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.014 | 0.026 |
| Insufficient payload (model declined to judge) | 0.005 | 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".