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Record W3048710252 · doi:10.5430/jnep.v10n11p76

Czech Republic consensus on recommendations to prevent and manage extravasation (paravasation) of cytotoxic drugs

2020· article· en· W3048710252 on OpenAlexvenueno aff
Samuel Vokurka, Viktor Maňásek, Darja Navrátilová Hrabánková, Simona Šípová, Zuzana Šustková, Lenka Turková, Erika Hajnová Fukasová, Zuzana Sýkorová, Šárka Kozáková, Jitka Wintnerová

Bibliographic record

VenueJournal of Nursing Education and Practice · 2020
Typearticle
Languageen
FieldMedicine
TopicChemotherapy-related skin toxicity
Canadian institutionsnot available
Fundersnot available
KeywordsExtravasationMedicineDexrazoxaneChemotherapyPharmacologyOncologyInternal medicineCancerAnthracyclineBreast cancerPathology

Abstract

fetched live from OpenAlex

Extravasation (paravasation) of cytotoxic chemotherapy drugs represents a very important complication in oncology nursing. Prevention and proper care can reduce the risk of extravasation and its consequences. A consensus on chemotherapy extravasation management have been reached within twenty seven oncology and haemato-oncology centres in the Czech Republic. Ensuring reliable, safe venous access, choice of injection site, venous line control, and patient education are integral and very important parts of care. DMSO (99%) is recommended for topical application after extravasation of anthracyclines, mitomycin C and cisplatin. Dry cold should be applied in case of DMSO-treated cisplatin extravasation, anthracyclines, mitomycin C and in extravasations of all other cytotoxic drugs (except for those with recommended dry heat applications). Dry heat is to be applied in cases of extravasation of oxaliplatin, taxanes and vinca-alkaloids. Hyaluronidase applied subcutaneously around the extravasation is recommended in the case of extravasation of taxanes and vinca-alkaloids. Dexrazoxane i.v. can be used when dealing with extravasation of anthracyclines. Corticosteroids applied subcutaneously, moist heat or cooling, are not recommended.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.758
Threshold uncertainty score0.307

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.090
GPT teacher head0.416
Teacher spread0.326 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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
Published2020
Admission routes1
Has abstractyes

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