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Record W2888500035

Gli indicatori di aderenza dei pazienti oncologici alla chemioterapia orale. Evidenze empiriche da una revisione sistematica della letteratura

2018· article· it· W2888500035 on OpenAlexaboutno aff
Maria Luisa Rega, Costanza Calabrese, P. Tortorella, Chiara de Waure, Gianfranco Damiani

Bibliographic record

VenuePROFESSIONI INFERMIERISTICHE · 2018
Typearticle
Languageit
FieldMedicine
TopicMedication Adherence and Compliance
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

Introduzione. L'indice di aderenza ha un ruolo fondamentale nel trattamento dei pazienti poiche influenza notevolmente l'efficacia di un trattamento terapeutico in termini di miglioramento della sopravvivenza globale, della speranza di vita, della qualiti  della stessa e di riduzione dei costi sanitari. Esistono lacune nell'individuazione degli indicatori da utilizzare per valutare l'aderenza e le modaliti  attraverso cui tali indicatori debbano essere adottati. L'obiettivo di questo lavoro e quello di individuare in letteratura gli indicatori di aderenza.Metodo. Revisione sistematica della letteratura di tipo quantitativo effettuata seguendo il metodo PRISMA. La ricerca e stata condotta su: Cinhal-EBSCO, Medline-PUBMED e Scopus. Sono stati ricercati studi che misurassero l'aderenza dei pazienti al trattamento in lingua inglese e pubblicati dal 2010 al 2016. La selezione e stata effettuate utilizzando criteri di inclusione ed esclusione. La qualiti  degli articoli e stata valutata con la NewCastle Ottawa Scale per gli studi osservazionali e con la Cochrane Collaboration Risk of Bias per gli studi sperimentali.Risultati. Sono stati ritrovati in totale 7,368 articoli e di questi selezionati 15 (11 studi osservazionali, 4 RCT), per un totale di 1,396 pazienti. Gli indicatori individuati sono: strumenti self-report, conta pillola, tasso di ricarica del farmaco, misure continue, dosaggio dei metaboliti. Un paziente oncologico e considerato aderente al trattamento se assume una percentuale di farmaci ≥ 80% rispetto ai farmaci prescritti.Discussione. Si ottiene una valutazione migliore di aderenza mediante l'utilizzo di piu strumenti contemporaneamente. Gli indicatori oggettivi derivano dall'utilizzo dei metodi di misurazione diretta dell'aderenza, quelli soggettivi da quella indiretta. Parole chiave: self-management support, oral chemioterapy, measure adherence, measure compliance.The Adherence's indicators of cancer patients to oral chemotherapy. A sistematic literature review.ABSTRACTIntroduction. Adherence has a key role in treating patients as influences the effectiveness of therapeutic treatment for improving overall survival, life expectancy, quality of life and reducing healthcare costs. There are gaps in identifying indicators to be used to evaluate adherence and ways in which these indicators should be adopted. The aim of this paper is to identify adherence's indicators in literature.Methods. Systematic review was carried out in, Cinhal-EBSCO, Medline-PUBMED and Scopus including studies of measure patient's adherence in English and published from 2010 to 2016. Inclusion and exclusion criteria were used. The quality of the articles was assessed with the NewCastle Ottawa Scale for observational studies and the Cochrane Collaboration Risk of Bias for experimental studies.Results. Of the 7,368 papers initially retrieved, 15 met the inclusion criteria (11 observational studies, 4 RCTs), for a total of 1,396 patients. The indicators found are: self-report tools, pill counts, drug recharge rate, continuous measures, metabolic dosage. A patient is considered adherent to the treatment if he or she assumes a percentage of drugs ≥ 80% of the prescribed medications. Discussion. A better adherence rating is obtained by using multiple instruments at the same time. The objective indicators derive from the direct measurement methods of adherence, the subjective ones from the indirect. Key words: self-management support, oral chemioterapy, measure adherence, measure compliance.

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)0.003
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.170
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0220.018

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 head0.106
GPT teacher head0.410
Teacher spread0.304 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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