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Challenges of implementing the Paediatric Surviving Sepsis Campaign International Guidelines 2020 in resource-limited settings: A real-world view beyond the academia

2021· article· es· W4206925902 on OpenAlexaff
Gavin Wooldridge, Nicole O’Brien, Fiona Muttalib, Qalab Abbas, John Adabie Appiah, Tim Baker, Sangita Basnet, Santiago Campos-Miño, Daniela Carla de Souza, Franco Díaz, Angela Dramowski, Jaime Fernández‐Sarmiento, Ana Fustiñana, Gustavo González, Roberto Jabornisky, Juan Camilo Jaramillo-Bustamante, Chor Yek Kee, Hans-Joerg Lang, Vanessa Soares Lanziotti, Guillermo Kohn Loncarica, Hadi Mohseni-Bod, Bunmi Ode, Srinivas Murthy, Amelie Von Saint Andre – von Arnim, Andreas Hansmann, Sebastián González‐Dambrauskas

Bibliographic record

VenueAndes pediatrica · 2021
Typearticle
Languagees
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsCanadian Red Cross SocietyHospital for Sick ChildrenBC Children's Hospital
Fundersnot available
KeywordsHumanitiesSepsisMedicinePolitical sciencePhilosophySurgery

Abstract

fetched live from OpenAlex

The Surviving Sepsis Campaign International Guidelines for the Management of Septic Shock and Sepsis-associated Organ Dysfunction in Children was released in 2020 and is intended for use in all global settings that care for children with sepsis. However, practitioners managing children with sep sis in resource-limited settings (RLS) face several challenges and disease patterns not experienced by those in resource-rich settings. Based upon our collective experience from RLS, we aimed to reflect on the difficulties of implementing the international guidelines. We believe there is an urgent need for more evidence from RLS on feasible, efficacious approaches to the management of sepsis and septic shock that could be included in future context-specific 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)0.130
metaresearch head score (Gemma)0.216
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.130
Threshold uncertainty score0.685

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1300.216
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.008
Scholarly communication0.0130.011
Open science0.0050.010
Research integrity0.0100.024
Insufficient payload (model declined to judge)0.0040.001

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 head0.090
GPT teacher head0.383
Teacher spread0.293 · 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

Citations10
Published2021
Admission routes1
Has abstractyes

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