Gli indicatori di aderenza dei pazienti oncologici alla chemioterapia orale. Evidenze empiriche da una revisione sistematica della letteratura
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.070 | 0.276 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".