Lessons learned from the data analysis of the second harvest (1998–2001) of the Society of Thoracic Surgeons (STS) Congenital Heart Surgery Database1
Bibliographic record
Abstract
OBJECTIVE: The analysis of the second harvest of the STS Congenital Heart Surgery Database produced meaningful outcome data and several critical lessons relevant to congenital heart surgery outcomes analysis worldwide. METHODS: This data harvest represents the first STS multi-institutional experience with software utilizing the nomenclature and database requirements adopted by the STS and EACTS (April 2000 Annals of Thoracic Surgery). Members of the STS Congenital Heart Committee analyzed the STS data. RESULTS: This STS harvest includes data from 16 centers (12787 cases, 2881 neonates, 4124 infants). In 2002, the EACTS reported similar outcome data utilizing the same database definitions (41 centers, 12736 cases, 2245 neonates, 4195 infants). Lessons from the analysis include: (1) Death must be clearly defined. (2) The Primary Procedure in a given operation must be documented. (3) Inclusionary and exclusionary criteria for all diagnoses and procedures must be agreed upon. (4) Missing data values remain an issue for the database. (5) Generic terms in the nomenclature lists, that is terms ending in Not Otherwise Specified (NOS), are redundant and decrease the clarity of data analysis. (6) Methodology needs to be developed and implemented to assure and verify data completeness and data accuracy. 'Operative Mortality' and 'Mortality Assigned to this Operation' were defined by the STS and EACTS; these definitions were not utilized uniformly. 'Thirty Day Mortality' was problematic because some centers did not track mortality after hospital discharge. Only 'Mortality Prior to Discharge' was consistently reported. Designation of Primary Procedure for a given operation determines its location for analysis. Until Complexity Scores lead to automated methodology for choosing the Primary Procedure, the surgeon must designate the Primary Procedure. Inclusionary and exclusionary criteria for all diagnoses and procedures have been developed in an effort to define acceptable concomitant diagnoses and procedures for each analysis. Improvements in data completeness can be achieved using a variety of techniques including developing more functional techniques of data entry at individual institutions and software improvements. Future versions of the STS Congenital Database will request that the coding of diagnoses and procedures avoid the terms ending in NOS. CONCLUSIONS: Lessons from this data harvest should improve congenital heart surgery outcome analysis.
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How this classification was reachedexpand
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 | 0.012 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".