RESPONSE LETTER TO DR. KIM AND MS. CHOI
Bibliographic record
Abstract
To the Editor: We would like to thank Kim and Choi for their comments.1 We welcome the opportunity to provide additional information omitted from our article because of space restrictions.2 Table 1 compares baseline characteristics of our study sample with the 6,594 patients undergoing coronary catheterization who fulfilled eligibility criteria during the recruitment period: aged 60 and older and no prior percutaneous coronary intervention (PCI) or coronary artery bypass graft (CABG) procedure. A higher proportion of patients with CABG and PCI (compared with overall population distributions) and those with stable angina pectoris at the time of the baseline assessment was purposefully enrolled, which explains the differences observed. Information about activities of daily living (ADLs) is not typically collected for catheterization patients. In our sample, few patients had baseline ADL disabilities (20 (5%) had 1, 7 (2%) had 2, and 6 (2%) had 3 or 4). We agree that a more comprehensive clinical prediction model could be developed, but the focus of the article was to fully describe the development of a frailty measure that will inform further research in full prediction models. A range of patient characteristics (clinical, cognitive, and psychosocial) is being examined to develop a full prediction model for functional decline at 30 months postprocedure. Regarding treatment type (CABG, PCI, or medical) and functional outcomes, no statistically significant relationship was found between treatment and the likelihood of decline at 12 months in function or health-related quality of life. In addition, treatment type neither modified nor confounded the relationship between the frailty index and either outcome. With regard to model performance, the Hosmer-Lemeshow test for the final 5-variable model (shown in Table 2 of the previous article) yielded a P-value of .76, indicating that calibration was good. This model was simplified by assigning indicators for the presence or absence of each of the criteria, giving each variable equal weight. Table 3 showed the results of modeling a 2 × 4 contingency table using the frailty score categories of 0, 1, 2, and 3 or more. This approach models the proportions exactly, so the predicted (or fitted) and observed risks are the same. We hope that this response has helped clarify the concerns brought forward by Dr. Kim and Ms. Choi. We thank them for their interest in our research. Conflict of Interest: The editor in chief has reviewed the conflict of interest checklist provided by the authors and has determined that the authors have no financial or any other kind of personal conflicts with this paper. Author Contributions: All authors contributed to this response. Sponsor's Role: NA.
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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 | 0.004 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.021 | 0.024 |
| Insufficient payload (model declined to judge) | 0.023 | 0.018 |
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".