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Record W3023875944 · doi:10.7759/cureus.8013

Integrating Survivorship Care Into a Radiation Medicine Program

2020· article· en· W3023875944 on OpenAlexaff
Shaziya Malam, Belinda Lawrence, Cari Bradley, Kimberly M McBride, Ashley Clement, Tatiana Conrad, M.Cheryl Noronha, Jeanette K Wong, Rachel Woo, Zahra Kassam

Bibliographic record

VenueCureus · 2020
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsSouthlake Regional Health Center
Fundersnot available
KeywordsSurvivorship curveMedicineSession (web analytics)Breast cancerCancer survivorshipPresentation (obstetrics)Program evaluationPhoneFamily medicineCancerSurgery

Abstract

fetched live from OpenAlex

Introduction An important but often overlooked component of caring for cancer patients is survivorship care, provided after the completion of active treatment in order to facilitate transition into the next surveillance phase. A survivorship program was developed to deliver a one-on-one education session on healthy lifestyle behaviours and available resources to help patients transition to their post-treatment life. This study reports the outcome of this pilot survivorship care program provided to breast cancer patients completing radiation therapy. Program delivery format and content were evaluated for effectiveness, applicability, and feasibility. Methods and materials Between March 2017 and August 2018, 124 breast cancer patients, nearing completion of their curative intent radiation treatments, participated in this centre-specific survivorship program. The survivorship program entailed a one on one education session delivered to breast cancer patients within the last two weeks of their radiation treatment. Participants were provided a Microsoft PowerPoint presentation, information pamphlet, and evaluation form to provide feedback on materials and presentation. Survivorship education sessions were delivered by study staff or staff scheduled in the Pre-Radiotherapy Patient Assessment role. Follow-up phone calls were conducted post-session delivery to determine the ongoing applicability of survivorship material. Staff was also given an evaluation form upon completion of the trial to measure the session feasibility. Results Of the 124 participants in the study, 69 (56%) provided feedback. Results showed that 98% of participants felt the information provided either confirmed what they were already doing (44%) or encouraged them to consider a lifestyle change (54%). Additionally, 70% reported feeling more confident after completing the session. Staff survey results reported that 87.5% agreed or strongly agreed that these sessions were beneficial and valuable to patients Conclusions Delivering one-on-one education sessions to individual participants focusing on healthy lifestyle measures garnered a positive response from participants, increasing their confidence and knowledge for making lifestyle changes. While staff survey results pointed strongly in favour of continuing with the survivorship sessions, it was shown that the methods of delivery trialed in this study were not feasible to be implemented on a larger scale. With some workflow modification, implementing a survivorship care program in our cancer centre is a possible and important aspect of a patient's treatment journey.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.031
GPT teacher head0.325
Teacher spread0.295 · 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".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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