Shaken baby syndrome: a medical, scientific, and legal controversy
Bibliographic record
Abstract
Among the various forms of violence against children, shaking is one of the most severe. It involves young infants shaken by adults, presumably overwhelmed by their crying. Identified half a century ago, this form of abuse can cause severe or fatal neurological injuries, prompting prevention campaigns in France and worldwide. While the reality and dangerous consequences of shaking are undisputed, the medical identification of shaken infants-based on various types of observations (notably radiological and anatomo-pathological)-has been debated in academic, medical, and legal circles since the 1990s. These discussions on the scientific reliability of such 'medical diagnoses of child abuse' are supported by a vast body of literature, even though some institutional actors minimize or reject them.This article presents a non-systematic review of these debates through a brief historical overview of the controversy and a concise discussion of the key medical, scientific, and legal issues. Significant developments have emerged in the understanding of shaken baby syndrome, including the nature and pathophysiology of associated injuries, the evidentiary value of available literature, and the forensic reliability of the diagnosis. The necessity of child protection explains why these academic debates are intertwined with legal, political, and societal issues. However, this same imperative calls for moving beyond polarization to achieve a more effective and reliable detection of child abuse.
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".