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Record W2794184955 · doi:10.1111/apa.14309

Childhood death rates declined in Sweden from 2000 to 2014 but deaths from external causes were not always investigated

2018· article· en· W2794184955 on OpenAlexaboutno aff
Gabriel Otterman, Klara Lahne, Elizabeth V. Arkema, Steven Lucas, Staffan Janson, Lena Hellström‐Westas

Bibliographic record

VenueActa Paediatrica · 2018
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
FundersBrottsoffermyndigheten
KeywordsMedicineDeath certificateDemographyCause of deathPediatricsQuarter (Canadian coin)Mortality ratePopulationEnvironmental healthSurgeryDisease

Abstract

fetched live from OpenAlex

AIM: Countries that conduct systematic child death reviews report a high proportion of modifiable characteristics among deaths from external causes, and this study examined the trends in Sweden. METHODS: We analysed individual-level data on external, ill-defined and unknown causes from the Swedish cause of death register from 2000 to 2014, and mortality rates were estimated for children under the age of one and for those aged 1-14 and 15-17 years. RESULTS: Child deaths from all causes were 7914, and 2006 (25%) were from external, ill-defined and unknown causes: 610 (30%) were infants, 692 (34%) were 1-14 and 704 (35%) were 15-17. The annual average was 134 cases (range 99-156) during the study period. Mortality rates from external, ill-defined and unknown causes in children under 18 fell 19%, from 7.4 to 6.0 per 100 000 population. A sizeable number of infant deaths (8.0%) were registered without a death certificate during the study period, but these counts were lower in children aged 1-14 (1.3%) and 15-17 (0.9%). CONCLUSION: Childhood deaths showed a sustained decline from 2000 to 2014 in Sweden and a quarter were from external, ill-defined or unknown causes. Systematic, interagency death reviews could yield information that could prevent future deaths.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.022
GPT teacher head0.289
Teacher spread0.266 · 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; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations10
Published2018
Admission routes1
Has abstractyes

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