Ten Percent of SIDS Cases are Murder — or are They?
Bibliographic record
Abstract
Sudden infant death syndrome (SIDS) has been used as a cause of death for over four decades. It has allowed deaths of infants to be registered as natural. Within this group of deaths, a certain number have been recognized to be homicides from inflicted smothering rather than being natural or accidental deaths. Research has been conducted using confidential inquires to determine how frequent homicide is in cases called SIDS. This paper traces the history of quoted rates of homicide. Early work suggested the figure was between 2-10% of all SIDS cases, though other workers have suggested figures as high as 20-40%. With the fall in the rate of infant deaths following the "Back to Sleep" campaigns, these figures have been reevaluated. If the higher figures were correct that 20-40% of SIDS were homicides, the fall in infant deaths would be expected to be less than it has been. Current data suggests a much lower figure than 10% of current cases, with much lower overall rates of infant deaths. As well as 10% of SIDS cases having been stated to be homicides, a related question is whether multiple deaths classified as SIDS are really homicides. The paper discusses the maxim that one death is a tragedy, two is suspicious, and three deaths indicate homicide. The paper also looks at court cases and the approach that has been made in prosecutions of sudden unexpected death in infancy as multiple murder.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".