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Record W2782678054 · doi:10.1007/s00520-017-4025-9

Humanistic burden of disease for patients with advanced melanoma in Canada

2018· article· en· W2782678054 on OpenAlexaffabout
Winson Y. Cheung, Martha Bayliss, Michelle K. White, Angela Stroupe, Andrew Lovley, Bellinda L. King‐Kallimanis, Kathryn Lasch

Bibliographic record

VenueSupportive Care in Cancer · 2018
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity of British ColumbiaBC Cancer Agency
FundersMelanoma Research Alliance
KeywordsMedicinePain medicineNursing researchDiseaseMelanomaIntensive care medicineFamily medicineInternal medicineNursingPsychiatryAnesthesiology

Abstract

fetched live from OpenAlex

BACKGROUND: Metastatic melanoma is a highly aggressive cancer, often striking in the prime of life. This study provides new information directly from advanced melanoma (stage III and IV) patients on how their disease impacts their health-related quality of life (HRQL). METHODS: Twenty-nine in-depth, qualitative interviews were conducted with adult patients with advanced melanoma in Canada. A semi-structured interview guide was used. Interviews were transcribed verbatim and key concepts were identified using a grounded theory analytic approach. RESULTS: Many patients' journeys began with the startling diagnosis of an invasive disease and a vastly shortened life expectancy. By the time they reached an advanced stage of melanoma, these patients' overall functioning and quality of life had been greatly diminished by this quickly progressing cancer. The impact was described in terms of physical pain and disability, emotional distress, diminished interactions with friends and family, and burden on caregivers. CONCLUSION: Our findings provide evidence of signs, symptoms, and functional impacts of advanced melanoma. Signs and symptoms reported (physical, mental, and social) confirm and expand on those reported in the existing clinical literature. Primary care physicians should be better trained to identify melanomas early. Oncology care teams can improve on their current approaches for helping patients navigate treatment options, with information about ancillary services to mitigate disease impacts on HRQL, such as mental health and social supports, as well as employment or financial support services.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.282
Threshold uncertainty score0.530

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.009
GPT teacher head0.278
Teacher spread0.269 · 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 designObservational
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

Citations18
Published2018
Admission routes2
Has abstractyes

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