Creating transparency in UK adult cardiac surgery data
Bibliographic record
Abstract
Cardiac surgeons in the UK have openly published their outcome data at hospital and individual surgeon levels since 2005. Publication of these data has been associated with a decreased risk of inhospital mortality following cardiac surgery despite more high risk patients undergoing surgery. Cardiac surgeons in the UK have now developed a series of different tools with the aim of further improving transparency, facilitating access to clinical data and driving quality improvement. These tools make contemporary high quality data from the National Adult Cardiac Surgery Audit (NACSA) database available to various different interest groups. This article describes the tools that have been developed and details how they can be accessed. The National Health Service (NHS) commissioning Board has recently announced that results for 10 specialties will be published at an individual team level by Summer 2013.1 The public inquiry into the events at the Mid Staffordshire NHS Foundation Trust has also recommended that clinical outcomes should be published more widely to assure the quality of healthcare services and help prevent further failures of clinical governance.2 UK cardiac surgeons first published their results in 2005 following a request by the Guardian newspaper under the newly introduced Freedom of Information Act.3 Following on from this, the Society for Cardiothoracic Surgery in Great Britain and Ireland (SCTS) has continued to conduct governance analyses and publish results at hospital and individual surgeon levels. This was done initially in conjunction with the Care Quality Commission but more recently results have been published on the SCTS website (http://www.scts.org/patients).4 The outcomes published in the public domain are based on an analysis of ‘all’ cardiac surgery over a 3-year window of …
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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 | 0.311 | 0.699 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.022 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.027 | 0.034 |
| Open science | 0.005 | 0.030 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".