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Developing a standardized approach to prevention and outpatient management of febrile neutropenia.

2022· article· en· W4298147445 on OpenAlexaffabout
Andrea Crespo, Daniela Gallo-Hershberg, Katherine Enright, Leta Forbes, Kathy Vu

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicNeutropenia and Cancer Infections
Canadian institutionsPublic Health OntarioCancer Care Ontario
Fundersnot available
KeywordsMedicineGuidelineQuality managementMultidisciplinary approachPatient educationFebrile neutropeniaAmbulatory careFamily medicineMEDLINEIntensive care medicineBest practiceHealth careNursingMedical emergencyNeutropeniaSurgeryManagement systemPathology

Abstract

fetched live from OpenAlex

234 Background: Optimal prevention and safe management of febrile neutropenia (FN) in the outpatient setting, when clinically appropriate, can help to keep vulnerable patients from experiencing severe complications requiring hospitalization. Variation in the prevention and outpatient management of FN was identified as a quality and safety gap by Ontario clinicians. This initiative aimed to facilitate a standardized approach to prevention and outpatient management of FN through the development of practical, evidence-based health-care provider and patient resources. Methods: A clinical practice guideline was developed by a 15-member multidisciplinary Working Group (WG) consisting of physicians, pharmacists, nurses, and administrators who are knowledgeable in the areas of prevention and management of FN. The WG reviewed current relevant international guidelines and available literature on primary and secondary FN prophylaxis and FN management, with an emphasis on the optimal use of granulocyte colony-stimulating factor (G-CSF) and appropriate FN management in the outpatient setting. The 2016 Cancer Care Ontario recommendations on the use of G-CSF were used as a foundation for the prevention of FN content. Key clinical questions were identified by the WG, and content was approached with an Ontario-specific lens. Recommendations were developed using an iterative consensus-building process over six WG meetings. The guideline report was reviewed by external clinical experts who validated final content. Accompanying patient information was informed by health literacy best practices, existing symptom management resources, and input from patient education experts, patients, and caregivers. Results: A user-friendly clinical practice guideline was created. Definitions, risk factors, FN prophylaxis strategies based on risk/treatment intent, and appropriate outpatient management of FN are described. Key clinical questions are reviewed and a total of 25 evidence-informed consensus-based recommendations are presented. The guideline includes FN risk assessment and outpatient FN management algorithms, as well as summaries of available literature and WG discussion. The accompanying patient information explains what neutropenia and neutropenic fever are, lists potential symptoms, and describes management strategies in patient-friendly language. All resources are publicly available on the Ontario Health-Cancer Care Ontario website and have been disseminated broadly to relevant stakeholders via email, social media, and webinars. Conclusions: Review of current evidence and expertise from oncology and infectious disease clinicians resulted in an evidence-informed, consensus-based guideline. This clinical practice guideline and accompanying patient information resource can help to facilitate safe, standardized prevention and appropriate outpatient management of FN.

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)0.040
metaresearch head score (Gemma)0.057
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.208
Threshold uncertainty score0.413

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0400.057
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0020.002
Scholarly communication0.0040.003
Open science0.0040.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0020.002

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 head0.164
GPT teacher head0.478
Teacher spread0.314 · 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 designNot applicable
Domainnot available
GenreMethods

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".

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Citations0
Published2022
Admission routes2
Has abstractyes

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