Cost-effectiveness of a high-sensitivity cardiac troponin T systematic screening strategy compared with usual care to identify patients with peri-operative myocardial injury after major noncardiac surgery: Erratum
Bibliographic record
Abstract
Due to an error at the Publisher's office, key information was missing from the Acknowledgements in the article, “Cost-effectiveness of a high-sensitivity cardiac troponin T systematic screening strategy compared with usual care to identify patients with peri-operative myocardial injury after major noncardiac surgery”.1 The full list of Acknowledgements is provided below: Authors’ contributions: Ekaterine Popova (EP): Conceptualization, methodology, data curation, investigation, writing – original draft, visualisation, project administration. Pablo Alonso-Coello (PAC): Conceptualization, methodology, writing – review & editing, supervision, funding acquisition. Jesús Alvarez-Garcia (JAG): Methodology, data curation, project administration, visualisation, writing, review & editing. Pilar Paniagua-Iglesias (PPI): Methodology, data curation, project administration, writing – review & editing. Montserrat Rué-Monné (MRM): Conceptualization, writing – review & editing. Miguel Vives- Borrás (MVB): Project administration, data curation, visualisation, writing – review & editing. Adria Font-Gual (AFG): Project administration, data curation, visualisation, writing – review & editing. Ignasi Gich-Saladich (IGS): Formal analysis, writing – review & editing. Cecilia Martinez Bru (CMB): Project administration, visualisation, writing – review & editing. Supervision, Jordi Ordoñez-Llanos (JOL): Conceptualization, writing – review & editing. Misericordia Carles-Lavila (MCL): Conceptualization, methodology, formal analysis, data curation, visualisation, writing – review & editing, supervision. Acknowledgements relating to this article Assistance with the study: we would like to thank all the patients that agreed to be included in the study and to all the personnel of our hospital that participated in its different steps. Thanks to the personnel of Department of Economics and Centre de Recerca en Economia i Sostenibilitat (ECO-SOS), Universitat Rovira i Virgili, Spain. Ekaterine Popova (EP) is a PhD candidate in the doctorate program of “Methodology of Biomedical Research and Public Health”, of the Pediatrics, Obstetrics and Gynecology and Preventive Medicine Department, Universitat Autònoma de Barcelona, Spain. Financial support and sponsorship: the study has been supported by a “Marato de TV3” grant (20150110) to Pablo Alonso-Coello and by Generalitat de Catalunya (PERIS SLT017/20/000089) to Ekaterine Popova. Conflict of interest: none. Presentation: preliminary data of this study were presented as a poster presentation at the Peri-operative Care Congress, November 11–14, 2021, Toronto, Canada. Funding statement: the study was supported by a research grant from Fundació La Marató de TV3 (20150110). Disclaimer: the funders had no role in the study design, data collection, management, analysis, writing of the report, the decision to submit the report for publication, and they will not have ultimate authority over any of these activities. Data availability statement: any data required to support the study can be supplied upon reasonable request. The data used in the present study is part of a larger dataset. The data not used for this manuscript will be employed in future manuscripts. Technical appendix, statistical code, and dataset available from the Dryad repository. The record is hereby corrected.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".