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Record W2913811884 · doi:10.1007/978-3-030-03143-5_3

Infrastructure and Organization of Adult Intensive Care Units in Resource-Limited Settings

2019· book-chapter· en· W2913811884 on OpenAlexaff
Alfred Papali, Neill K. J. Adhikari, Janet Dı́az, Arjen M. Dondorp, Martin W. Dünser, Shevin T. Jacob, Jason Phua, Marc Romain, Marcus J. Schultz

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersMahidol UniversityEuropean Society of Intensive Care MedicineUniversity of OxfordWellcome Trust
KeywordsMedicineBackupStaffingPersonal protective equipmentContext (archaeology)Medical emergencyIntensive care medicineHygieneVital signsInfectious disease (medical specialty)NursingCoronavirus disease 2019 (COVID-19)DiseaseSurgeryComputer science

Abstract

fetched live from OpenAlex

In this chapter, we provide guidance on some basic structural requirements, focusing on organization, staffing, and infrastructure. We suggest a closed-format intensive care unit (ICU) with dedicated physicians and nurses, specifically trained in intensive care medicine whenever feasible. Regarding infrastructural components, a reliable electricity supply is essential, with adequate backup systems. Facilities for oxygen therapy are crucial, and the choice between oxygen concentrators, cylinders, and a centralized system depends on the setting. For use in mechanical ventilators, a centralized piped system is preferred. Facilities for proper hand hygiene are essential. Alcohol-based solutions are preferred, except in the context of Ebola virus disease (chloride-based solutions) and Clostridium difficile infection (soap and water). Availability of disposable gloves is important for self-protection; for invasive procedures masks, caps, sterile gowns, sterile drapes, and sterile gloves are recommended. Caring for patients with highly contagious infectious diseases requires access to personal protective equipment. Basic ICU equipment should include vital signs monitors and mechanical ventilators, which should also deliver noninvasive ventilator modes. We suggest that ICUs providing invasive ventilatory support have the ability to measure end-tidal carbon dioxide and if possible can perform blood gas analysis. We recommend availability of glucometers and capabilities for measuring blood lactate. We suggest implementation of bedside ultrasound as diagnostic tool. Finally, we recommend proper administration of patient data; suggest development of locally applicable bundles, protocols, and checklists for the management of sepsis; and implement systematic collection of quality and performance indicators to guide improvements in ICU performance.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0330.004

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 head0.020
GPT teacher head0.248
Teacher spread0.228 · 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

Citations20
Published2019
Admission routes1
Has abstractyes

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