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Record W2127069165 · doi:10.1177/2150135113519454

Linking the Congenital Heart Surgery Databases of the Society of Thoracic Surgeons and the Congenital Heart Surgeons’ Society

2014· article· en· W2127069165 on OpenAlexaff
Jeffrey P. Jacobs, Sara K. Pasquali, Erle H. Austin, J. William Gaynor, Carl L. Backer, Jennifer C. Hirsch‐Romano, William G. Williams, Christopher A. Caldarone, Brian W. McCrindle, Karen E. Graham, Rachel S. Dokholyan, Gregory J. Shook, Jennifer Poteat, Maulik V. Baxi, Tara Karamlou, Eugene H. Blackstone, Constantine Mavroudis, John E. Mayer, Richard A. Jonas, Marshall L. Jacobs

Bibliographic record

VenueWorld Journal for Pediatric and Congenital Heart Surgery · 2014
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsHospital for Sick Children
FundersNational Heart, Lung, and Blood Institute
KeywordsMedicineCardiac surgeryPediatricsDatabaseSurgeryComputer science

Abstract

fetched live from OpenAlex

PURPOSE: The Society of Thoracic Surgeons Congenital Heart Surgery Database (STS-CHSD) is the largest Registry in the world of patients who have undergone congenital and pediatric cardiac surgical operations. The Congenital Heart Surgeons' Society Database (CHSS-D) is an Academic Database designed for specialized detailed analyses of specific congenital cardiac malformations and related treatment strategies. The goal of this project was to create a link between the STS-CHSD and the CHSS-D in order to facilitate studies not possible using either individual database alone and to help identify patients who are potentially eligible for enrollment in CHSS studies. METHODS: Centers were classified on the basis of participation in the STS-CHSD, the CHSS-D, or both. Five matrices, based on CHSS inclusionary criteria and STS-CHSD codes, were created to facilitate the automated identification of patients in the STS-CHSD who meet eligibility criteria for the five active CHSS studies. The matrices were evaluated with a manual adjudication process and were iteratively refined. The sensitivity and specificity of the original matrices and the refined matrices were assessed. RESULTS: In January 2012, a total of 100 centers participated in the STS-CHSD and 74 centers participated in the CHSS. A total of 70 centers participate in both and 40 of these 70 agreed to participate in this linkage project. The manual adjudication process and the refinement of the matrices resulted in an increase in the sensitivity of the matrices from 93% to 100% and an increase in the specificity of the matrices from 94% to 98%. CONCLUSION: Matrices were created to facilitate the automated identification of patients potentially eligible for the five active CHSS studies using the STS-CHSD. These matrices have a sensitivity of 100% and a specificity of 98%. In addition to facilitating identification of patients potentially eligible for enrollment in CHSS studies, these matrices will allow (1) estimation of the denominator of patients potentially eligible for CHSS studies and (2) comparison of eligible and enrolled patients to potentially eligible and not enrolled patients to assess the generalizability of CHSS studies.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.072
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.072
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0160.016
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0020.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.033
GPT teacher head0.296
Teacher spread0.263 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations35
Published2014
Admission routes1
Has abstractyes

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