A firm recommendation: measuring the softness of infant sleep surfaces
Bibliographic record
Abstract
Abstract Background Approximately 3600 sudden unexpected infant deaths (SUID) occur annually in the United States, and a quarter of SUIDs are caused by unintentional suffocation and strangulation in bed, with soft bedding use being a significant risk factor. Therefore, The American Academy of Pediatrics (AAP) recommends infants sleep on a “firm” surface, though neither an objective definition nor national standard has been established. The purpose of this study is to report on the performance of a device that measures mattress softness and to provide quantitative values of softness for various infant sleep surfaces. Methods In collaboration with the authors and a national child product safety organization (Kids in Danger), University of Michigan engineering students designed and validated a device that measures the vertical depression (softness) of a simulated 2-month-old’s head on a sleep surface. A total of 17 infant sleep surfaces − 14 household surfaces and 3 hospital mattresses - were measured between April 2019 and January 2020. The average softness of each surface was calculated. Surfaces were also measured with soft bedding, which included an infant fleece blanket, and firm and soft pillows. Results The average softness for the 14 household sleep surfaces ranged from 7.4–36.9 mm. The 2019 cribette playard and the 2018 infant spring had similar softness (21 mm) as the 2018 and 2019 adult foam and 2015 sofa. An infant’s fleece blanket folded once added an additional 2.3–6.5 mm of softness, folded twice added 4.8–11.6 mm, and folded three times added 11–21.8 mm. Using a firm pillow added 4.0–20.9 mm of softness while using a soft pillow added 24.5–46.4 mm. The softness for the 3 hospital sleep surfaces ranged from 14 to 36.9 mm, with the infant bassinet being the firmest and the pediatrics mattress being the softest. Conclusions We found a wide range of softness among sleep surfaces, with some infant mattresses as soft as some adult mattresses. Adding blankets and pillows to mattresses measurably increased softness. Quantifying sleep surface softness will advance our understanding of how softness relates to SUID risk. We hope this new information will further inform safe infant sleep recommendations and improve mattress safety standards nationally.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".