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Record W2114420547 · doi:10.1111/ecc.12408

A multi‐method review of home‐based chemotherapy

2015· review· en· W2114420547 on OpenAlexafffundabout
Jenna M. Evans, Mary Qiu, M. MacKinnon, E. Green, Katia Peterson, Laura Grau

Bibliographic record

VenueEuropean Journal of Cancer Care · 2015
Typereview
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsHome and Community Care Support ServicesCancer Care OntarioUniversity of Toronto
FundersCancer Care Ontario
KeywordsMedicineTeamworkService delivery frameworkNursingService (business)Work (physics)Family medicine

Abstract

fetched live from OpenAlex

This study summarises research- and practice-based evidence on home-based chemotherapy, and explores existing delivery models. A three-pronged investigation was conducted consisting of a literature review and synthesis of 54 papers, a review of seven home-based chemotherapy programmes spanning four countries, and two case studies within the Canadian province of Ontario. The results support the provision of home-based chemotherapy as a safe and patient-centred alternative to hospital- and outpatient-based service. This paper consolidates information on home-based chemotherapy programmes including services and drugs offered, patient eligibility criteria, patient views and experiences, delivery structures and processes, and common challenges. Fourteen recommendations are also provided for improving the delivery of chemotherapy in patients' homes by prioritising patient-centredness, provider training and teamwork, safety and quality of care, and programme management. The results of this study can be used to inform the development of an evidence-informed model for the delivery of chemotherapy and related care, such as symptom management, in patients' homes.

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)0.009
metaresearch head score (Gemma)0.024
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: Review
Teacher disagreement score0.020
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0200.021
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.309
GPT teacher head0.548
Teacher spread0.239 · 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

Citations56
Published2015
Admission routes3
Has abstractyes

Explore more

Same venueEuropean Journal of Cancer CareSame topicPalliative Care and End-of-Life IssuesFrench-language works237,207