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Record W2120158707 · doi:10.1111/chd.12297

Using Data to Improve Quality: the Pediatric Cardiac Care Consortium

2015· article· en· W2120158707 on OpenAlexfundno aff
James H. Moller

Bibliographic record

VenueCongenital Heart Disease · 2015
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsnot available
FundersPartenariat Canadien Contre Le Cancer
KeywordsMedicineMultivariate analysisEmergency medicineData qualityMedical emergencyQuality managementMortality rateCardiac catheterizationDatabasePediatricsOperations managementSurgeryInternal medicine

Abstract

fetched live from OpenAlex

A program to collect and analyze cardiac catheterization, electrophysiologic studies and cardiac operations in children was initiated in 1982. The purpose was to help centers compare their experience and outcomes with a group of centers to determine areas where their performance might improve. Cardiac centers became members of the Pediatric Cardiac Care Consortium and submitted demographic data and copies of procedure reports regularly to a central office. Data were extracted from the reports, coded by trained coders and entered into a computer database. Annually, the data were analyzed to compare the experience of an individual center with that of the entire group of centers. The annual data were adjusted for severity on the basis of eight factors selected after discussion with participants in the Consortium. Adjustment was by multivariate analysis. Reports were prepared for each center and distributed at an annual meeting. The data were used by centers to review operations where the mortality rate exceeded +2 standard deviations of the group. With discussion, the center staff often initiated changes to improve outcome. The outcome could then be monitored by the annual reports. Our data were also utilized in the creation of the Risk Adjustment for Surgery for Congenital Heart Disease (RACHS)-1 categories of disease severity. The mortality rates of our centers were comparable with the combined hospital discharge data from New York, Massachusetts, and California. From 1982 through 2007, the mortality rates of our centers dropped for each RACHS-1 category, falling to less than 1% for categories 1 and 2 for the last 5-year period. During the 25 years, we received data from 52 centers about 137 654 patients who underwent 117 756 cardiac operations.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.150
GPT teacher head0.394
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2015
Admission routes1
Has abstractyes

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