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Record W2171070622 · doi:10.1503/cmaj.051671

Sudden infant death syndrome

2006· review· en· W2171070622 on OpenAlexvenueno aff
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Bibliographic record

VenueCanadian Medical Association Journal · 2006
Typereview
Languageen
FieldNeuroscience
TopicNeuroscience of respiration and sleep
Canadian institutionsnot available
Fundersnot available
KeywordsSudden infant death syndromeComputer scienceMedicineData scienceWorld Wide WebPediatrics

Abstract

fetched live from OpenAlex

Sudden infant death syndrome (SIDS) continues to be the most common cause of postneonatal infant death. SIDS is a complex, multifactorial disorder, the cause of which is still not fully understood. However, much is known now about environmental risk factors, some of which are modifiable. These include maternal and antenatal risk factors such as smoking during pregnancy, as well as infant-related risk factors such as non-supine sleeping position and soft bedding. Emerging evidence also substantiates an expanding number of genetic risk factors. Interactions between environmental and genetic risk factors may be of critical importance in determining an infant's actual risk of SIDS. Although no practical way exists to identify which infants will die of SIDS, nor is there a safe and proven prevention strategy even if identification were feasible, reducing exposure to modifiable risk factors has helped to lower the incidence of SIDS. Current challenges include wider dissemination of guidelines to all people who care for infants, dissemination of guidelines in culturally appropriate ways, and surveillance of SIDS trends and other outcomes associated with implementation of these guidelines.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.007

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.

Opus teacher head0.040
GPT teacher head0.308
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations218
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Medical Association JournalSame topicNeuroscience of respiration and sleepFrench-language works237,207