Databases for assessing the outcomes of the treatment of patients with congenital and paediatric cardiac disease – a comparison of administrative and clinical data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The introduction of the reporting of medical and surgical outcomes to the public and the potential implementation of initiatives involving pay-for-performance have invigorated debates about the relative benefits of administrative and clinical databases for comparing rates of mortality at the level of the hospital and surgeon. While general agreement exists that public performance report cards must use the highest quality data available, debate continues regarding whether administrative or clinical data should be utilized for this purpose. Clinical databases may contain information more relevant to risk-adjustment, but the currently available clinical databases are voluntary and suffer from validity concerns. Administrative data, however, suffer from inaccuracies of coding and a lack of potentially informative covariates. Particularly problematic to congenital heart surgery is the non-uniform application of coding algorithms to define complex reconstructive procedures for which there is no unique code assignment. The purposes of this manuscript are; therefore, to discuss the relative advantages and limitations of both clinical and administrative data, and to provide a brief introduction to currently available databases germane to the study of congenital cardiac disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it