Bibliographic record
Abstract
View this article online at wileyonlinelibrary.com. Potential conflict of interest: Nothing to report. We thank Sonoo et al. for their interest in our recent publication and for their correspondence. The use of large national administrative databases contains the inherent risk of bias and error due to the construction of the database. The sensitivity of palliative care code V66.7 has always been a limitation in the analysis of inpatient palliative care referrals. The article by Hua et al. reported on a single center study which employed the definition of “moderate to severe liver disease” from the Charlson Comorbidity Index.1 This is a poor definition of end‐stage liver disease and likely does not represent the patient cohort we attempted to characterize. In addition, Hua et al. do not define the size of the liver disease cohort.2 Nevertheless, the imperfect nature of the sensitivity of the V66.7 code does introduce some degree of uncertainty, which we acknowledged as a limitation in our article. While the type of insurance coverage may be heterogeneously dispersed among patients, we controlled for annual income, race, and other patient‐level variables in a predefined cohort of patients with end‐stage liver disease. We did not use multilevel adjusted models as this would interrupt the integrity of the National Inpatient Sample by defining a nested cohort, as suggested against by the Healthcare Cost and Utilization Project statistical brief cited by the authors.3 While many statistical methods are available to attempt to arrive at the most accurate inference of an analysis, we feel the findings of our study were completed after a rigorous and well‐designed analysis plan that went through a peer review process. Lastly, we must emphasize that the current analysis was to identify a problem, i.e., palliative care referrals in end‐stage liver disease patients, that is currently not well characterized. We did not intend for the article to inform public health policy decisions as the current analysis would be a suboptimal method to do so. We thank the authors for their interest in our work and hope that our reply has satisfied their concerns regarding our article.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.183 | 0.144 |
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 source (direct Gemma or distilled Codex), 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".