In Reply:
Bibliographic record
Abstract
We thank the authors for their interest in our work and highlighting the limitations of the kappa value. Cohen's kappa statistic is calculated as a ratio of observed and expected (chance) agreement,1 and we concur that this value is dependent on the number of categories and prevalence in each. Indeed, prevalence approaching 0 or 100% results in high chance agreement, which typically reduces or handicaps the kappa value as the authors correctly identified in the example provided. Thus, such an argument is often raised for justifying a low kappa value in the face of high observed agreement. We do not, however, see how that invalidates a high kappa value. Applying the above principles to our study, despite high chance agreement between reviewers in several categories while using HEARTSMAP to evaluate psychosocial documentation, our high observed agreement between reviewers overcame this handicap and resulted in kappa values representative of good to perfect agreement (Table 1). Therefore, we are confident that our measure of agreement substantiates our conclusion that the HEARTSMAP tool can be reliably used to assess pediatric psychosocial documentation in the emergency department.2
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 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 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.032 | 0.042 |
| Insufficient payload (model declined to judge) | 0.012 | 0.011 |
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".