Bibliographic record
Abstract
I thank the authors for their thoughtful letter and invigorating critique of routine screening for adverse childhood experiences (ACEs). This is a rapidly developing field, and one that warrants urgent attention to ensure best practices and effective interventions. ACEs are a growing public health priority due to their high prevalence and serious sequelae in childhood and across the lifespan. The brief ‘Practical Tips for Paediatricians’ format of the initial article did not allow for an exhaustive review of the evidence of the toxic effects of ACEs, but this has been well established elsewhere (1), motivating governmental bodies and professional practice associations such as the American Academy of Pediatrics to call for ACEs screening in conjunction with other initiatives to educate and support clinicians (2). Screening is indicated for ACEs because, unlike developmental delays, ACEs typically go undetected in childhood (3,4). Pediatric ACEs screening has been shown to be feasible by clinicians and acceptable to patients (5,6). The authors state that there are “no evidenced-based interventions tied to scores on an ACEs checklist.” However, as described in the Practical Tips column and other literature (7,8), simply arriving at an aggregate ACE score is not the goal; rather, screening is intended to prompt and inform a subsequent conversation with the child/youth and their caregiver about their specific experiences and needs. This conversation then guides the “pathway to accessing evidence-based child and parent mental health interventions”.
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 | 0.024 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.017 |
| Scholarly communication | 0.009 | 0.022 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.041 | 0.069 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".