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Record W2114561279 · doi:10.1016/j.aogh.2015.02.707

Stillbirth inequalities among American Indians and Alaska Natives, 2003-2012

2015· article· en· W2114561279 on OpenAlexaff
Kyle E. Freese, Lisa M. Bodnar, Jennifer A. Hutcheon

Bibliographic record

VenueAnnals of Global Health · 2015
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsChildren's & Women's Health Centre of British Columbia
Fundersnot available
KeywordsGeneral partnershipTriageCapacity buildingMedical educationTel avivMedicineNursingBusinessPolitical scienceMedical emergencyComputer science

Abstract

fetched live from OpenAlex

country programs. USAMC staff with necessary expertise and experience conducted the project. High-level SSAAMC assessments were carried out followed by the selection of two sites for two 10-day surgical care, teaching and capacity building pilots. In addition to utilizing standard data collection tools, the design of the assessment itself was a means to gather additional data relevant to assessing capacity building objectives. To collaboratively test surgical feasibility, USAMC faculty and trainees worked shoulder-to-shoulder with SSAAMC staff to triage patients, conduct surgeries, provide postoperative care, and establish treatment plans. Additionally, the USAMC team led didactic presentations and participated in surgical rounds. SSAAMC and USAMC leadership evaluated relative value of a partnership and subsequently developed long-term, shared program goals assuring program ownership by all parties.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.119
GPT teacher head0.496
Teacher spread0.377 · 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 teacher head, not a consensus.

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

Citations0
Published2015
Admission routes1
Has abstractyes

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