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Record W2968579279 · doi:10.1111/ppe.12574

Severe maternal morbidity surveillance: Monitoring pregnant women at high risk for prolonged hospitalisation and death

2019· article· en· W2968579279 on OpenAlexafffundabout
Susie Dzakpasu, Paromita Deb‐Rinker, Laura Arbour, Elizabeth Darling, Michael S. Kramer, Shiliang Liu, Wei Luo, Phil Murphy, Chantal Nelson, Joel G. Ray, Heather Scott, Michiel VandenHof, K.S. Joseph

Bibliographic record

VenuePaediatric and Perinatal Epidemiology · 2019
Typearticle
Languageen
FieldMedicine
TopicMaternal and fetal healthcare
Canadian institutionsDalhousie UniversityUniversity of TorontoMcGill University Health CentreMcMaster UniversityMcGill UniversityUniversity of British ColumbiaPublic Health Agency of Canada
FundersCanadian Institutes of Health ResearchBC Children's Hospital
KeywordsMedicineCase fatality rateConfidence intervalPopulationEclampsiaMaternal morbidityHELLP syndromeObstetricsPregnancyPediatricsEmergency medicineInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: There is no international consensus on the definition and components of severe maternal morbidity (SMM). OBJECTIVES: To propose a comprehensive definition of SMM, to create an empirically justified list of SMM types and subtypes, and to use this to examine SMM in Canada. METHODS: Severe maternal morbidity was defined as a set of heterogeneous maternal conditions known to be associated with severe illness and with prolonged hospitalisation or high case fatality. Candidate SMM types/subtypes were evaluated using information on all hospital deliveries in Canada (excluding Quebec), 2006-2015. SMM rates for 2012-2016 were quantified as a composite and as SMM types/subtypes. Rate ratios and population attributable fractions (PAF) associated with overall and specific SMM types/subtypes were estimated in relation to length of hospital stay (LOS > 7 days) and case fatality. RESULTS: There were 22 799 cases of SMM subtypes (among 1 418 545 deliveries) that were associated with a prolonged LOS or high case fatality. Between 2012 and 2016, the composite SMM rate was 16.1 (95% confidence interval [CI] 15.9, 16.3) per 1000 deliveries. Severe pre-eclampsia and HELLP syndrome (514.6 per 100 000 deliveries), and severe postpartum haemorrhage (433.2 per 100 000 deliveries) were the most common SMM types, while case fatality rates among SMM subtypes were highest among women who had cardiac arrest and resuscitation (241.1 per 1000), hepatic failure (147.1 per 1000), dialysis (67.6 per 1000), and cerebrovascular accident/stroke (51.0 per 1000). The PAF for prolonged hospital stay related to SMM was 17.8% (95% CI 17.3, 18.3), while the PAF for maternal death associated with SMM was 88.0% (95% CI 74.6, 94.4). CONCLUSIONS: The proposed definition of SMM and associated list of SMM subtypes could be used for standardised SMM surveillance, with rate ratios and PAFs associated with specific SMM types/subtypes serving to inform clinical practice and public health policy.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.311
Threshold uncertainty score0.618

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.300
Teacher spread0.272 · 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 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

Citations119
Published2019
Admission routes3
Has abstractyes

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