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Record W6963035548 · doi:10.17605/osf.io/2ca7g

PATIENT SAFETY IN MAJOR ONCOLOGICAL EMERGENCIES: SCOPING REVIEW

2023· other· en· W6963035548 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Science Framework · 2023
Typeother
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsnot available
Fundersnot available
KeywordsGrey literaturePatient safetyHealth careMEDLINEContext (archaeology)Latin AmericansSystematic reviewInclusion (mineral)

Abstract

fetched live from OpenAlex

PROTOCOL - SCOPING REVIEW PATIENT SAFETY IN MAJOR ONCOLOGICAL EMERGENCIES: SCOPING REVIEW Objective: Identify and synthesize scientific evidence on patient safety in major oncological emergencies. Question wording P (Population) – cancer patient C (Concept) – patient safety C (Context) – nurse care during major oncological emergencies What scientific evidence, in the context of nursing care in major oncological emergencies, is available to ensure patient safety? Inclusion criteria Will be included: studies published in full in English, Spanish and Portuguese; dealing with patient safety in oncological emergencies, published from 2013 to date (Ordinance No. 874/GM/MS, of May 16, 2013: National Policy for Cancer Prevention and Control in the Health Care Network) Health of People with Chronic Diseases within the Unified Health System (SUS). Exclusion criteria Will be excluded: single case studies, editorials, experience reports, annals of events, theoretical essays, narrative literature review. Data collect Data base: - Medline via the National Library of Medicine and National Institutes of Health (PubMed); - Latin American and Caribbean Literature in Health Sciences (LILACS), - SCOPUS; - Web of Science; - Base; - Cochrane Library. Gray literature search: - CAPES Theses and Dissertations Catalog - DART-Europe E-Theses Portal - Electronic Theses Online Service (EThOS) - Open Access Scientific Repository of Portugal (RCAAP) - National ETD Portal - Theses Canada - Latin American Thesis Portal - World Cat Dissertations and Theses • Identification of descriptors and keywords Descriptors and keywords used in studies that address the topic of interest from the combination of MeSH identified for the research mneumonic: (Acute tumor lysis syndrome OR Metabolic Diseases OR Syndrome of inappropriate secretion of antidiuretic hormone OR Hydroelectrolytic disorders OR Diabetes insipidus OR Malignant bowel obstruction OR Superior Vena Cava Syndrome OR Superior Mediastinal Syndrome OR Leukocytosis OR Chemotherapy-Induced Febrile Neutropenia OR Febrile Neutropenia OR Spinal Cord Compression OR Airway Obstruction) AND (Patient Safety [Mesh] OR Risk Management [Mesh] OR Medication Errors [Mesh] ) AND (Neoplasms [Mesh] OR Oncology Nursing [Mesh] OR Oncology [Mesh]).

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)0.074
metaresearch head score (Gemma)0.077
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.085
Threshold uncertainty score0.391

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0740.077
Meta-epidemiology (narrow)0.0050.005
Meta-epidemiology (broad)0.0120.010
Bibliometrics0.0180.013
Science and technology studies0.0060.005
Scholarly communication0.0090.010
Open science0.0060.007
Research integrity0.0100.007
Insufficient payload (model declined to judge)0.0850.016

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 head0.201
GPT teacher head0.493
Teacher spread0.292 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations1
Published2023
Admission routes1
Has abstractyes

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