Bibliographic record
Abstract
I thank the authors for their letter, and the Journal for a further opportunity to respond. Adverse childhood experiences (ACEs) and other developmental traumas are now beginning to receive much-needed scientific and public policy attention. The authors’ notable work in this field has been an invaluable contribution to understanding the impacts and high prevalence of childhood trauma. While we disagree on the need and timeliness of ACEs screening initiatives (1,2), scientific progress depends on discourse such as this to advance the field of research in childhood trauma, for the benefit of the children we are all trying to help. We agree that health care providers must be aware of ACEs and address them in an evidence-based way. We also agree that rigorous evaluation must be undertaken of all initiatives in childhood trauma, including screening, with an emphasis on health outcomes. Such research is currently underway, with promising results supporting screening: a recent systematic review of randomized controlled trials that involved detecting and addressing ACEs in children found that “five of six studies that directly involved pediatric primary care practices improved outcomes, including three trials that involved screening for ACEs” (3).
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 | 0.023 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.015 | 0.022 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".