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Record W3161671472 · doi:10.1016/j.xjon.2021.05.004

Commentary: One system to rule them all

2021· editorial· en· W3161671472 on OpenAlexaboutno aff
Valerie X. Du, Shawn S. Groth

Bibliographic record

VenueJTCVS Open · 2021
Typeeditorial
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsScopusMedicineHarmonizationMEDLINEAdverse effectCardiothoracic surgeryEsophagectomyCitationDatabaseSurgeryLibrary sciencePolitical scienceCancerEsophageal cancerComputer scienceInternal medicine

Abstract

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Central MessageLack of consistent definitions for adverse events contributes to challenges comparing outcomes and combining information across large clinical databases; harmonization of these definitions is needed.See Article page 250. Lack of consistent definitions for adverse events contributes to challenges comparing outcomes and combining information across large clinical databases; harmonization of these definitions is needed. See Article page 250. Over the past 2 decades, the use of large, national clinical registries and administrative datasets has played an increasingly important role in thoracic surgery health services research.1Groth S.S. Habermann E.B. Massarweh N.N. United States Administrative Databases and Cancer Registries for Thoracic Surgery Health Services Research.Ann Thorac Surg. 2020; 109: 636-644Abstract Full Text Full Text PDF PubMed Scopus (9) Google Scholar In particular, postoperative complications (or adverse events [AEs]) are important metrics used in quality improvement initiatives and have implications on patient-centered outcomes.2Tevis S.E. Kennedy G.D. Postoperative complications and implications on patient-centered outcomes.J Surg Res. 2013; 181: 106-113Abstract Full Text Full Text PDF PubMed Scopus (61) Google Scholar However, the various database entities, such as the Society of Thoracic Surgeons (STS), European Society of Thoracic Surgeons (ESTS), the Esophagectomy Complications Consensus Group, the National Surgical Quality Improvement Program, and the Thoracic Morbidity and Mortality, which was adopted by the Canadian Association of Thoracic Surgeons, have unique definitions for and classifications of AEs. This longstanding problem of a lack of uniform and consistent definitions for AEs contributes to challenges when attempting to compare outcomes and combine information across these datasets. In this issue of the Journal, Sigler and colleagues3Sigler G. Anstee C. Seely A. Harmonization of adverse events monitoring following thoracic surgery: pursuit of a common language and methodology.J Thorac Cardiovasc Surg Open. 2021; 162: 250-256Google Scholar begin to tackle this challenge by presenting a standardized method for AE documentation consisting of a single set of drop-down menu options for classification of AEs. With their proposed modifications, the degree of harmonization with Canadian Association of Thoracic Surgeons increased with the ESTS (100%), STS (from 89% to 93%), Esophagectomy Complications Consensus Group Esophagectomy Complications Consensus Group (from 74% to 86%), and National Surgical Quality Improvement Program (from 73% to 91%) databases. The authors should be congratulated for their efforts to create a framework to unify the definitions of AEs among these data stakeholders. However, it should be recognized that the intent of these effort is not synonymous with linking databases. Currently, if an institution wishes to participate in multiple databases, registrars have to fill in AEs using separate systems, which is expensive and time-consuming. This particular limitation of AE reporting across systems is what the authors begin to address. In other words, the system proposed by Sigler and colleagues3Sigler G. Anstee C. Seely A. Harmonization of adverse events monitoring following thoracic surgery: pursuit of a common language and methodology.J Thorac Cardiovasc Surg Open. 2021; 162: 250-256Google Scholar simply makes it easier for a particular institution to simultaneously participate in AE reporting for multiple databases and potentially enables pooling of AE data. While this is a step in the right direction, limitations and challenges remain. Changes in the definitions of AEs over time create difficulties when performing longitudinal studies within a database. In addition, it should be noted that the authors’ proposal for harmonization is neither based on rigorous research nor the collective agreement of experts from multiple groups of stakeholders; it is based on the opinion of 2 authors. While a utopian world for thoracic surgery health services research would include the ability to link all large data sets with ease, there are practical and financial limitations. Finally, when attempting to compare data between datasets, one must recognize that each dataset has unique intents, strengths, and limitations and includes particular populations.1Groth S.S. Habermann E.B. Massarweh N.N. United States Administrative Databases and Cancer Registries for Thoracic Surgery Health Services Research.Ann Thorac Surg. 2020; 109: 636-644Abstract Full Text Full Text PDF PubMed Scopus (9) Google Scholar Nonetheless, we should recognize that we are an international community of cardiothoracic surgeons who should engage in more collaborative efforts, such as the STS-ESTS database, and strive to maximize the potential of these large datasets to optimize our outcomes and the quality of care we provide to our patients. Harmonization of adverse events monitoring following thoracic surgery: Pursuit of a common language and methodologyJTCVS OpenVol. 6PreviewThoracic surgery carries significant risk of postoperative adverse events (AEs). Multiple international recording systems are used to define and collect AEs following thoracic surgery procedures. We hypothesized that a simple-yet-ubiquitous approach to AE documentation could be developed to allow universal data entry into separate international databases. Full-Text PDF Open Access

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.188
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.033
GPT teacher head0.322
Teacher spread0.289 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations0
Published2021
Admission routes1
Has abstractyes

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