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Record W2159876522 · doi:10.3747/co.20.1307

A Pilot Study Examining the Unmet Needs of Cancer Survivors Living with Polypathology

2013· article· en· W2159876522 on OpenAlexafffundvenueabout
Kin Wai Michael Siu, Pamela Catton, Janelle Jones, Alejandro R. Jadad

Bibliographic record

VenueCurrent Oncology · 2013
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsPublic Health OntarioUniversity of TorontoPrincess Margaret Cancer CentreOccupational Cancer Research CentreUniversity Health Network
FundersUniversity of TorontoPrincess Margaret Cancer FoundationCanada Research ChairsUniversity Health Network
KeywordsMedicineGerontologyFamily medicineNeeds assessmentCancer survivorAffect (linguistics)PopulationCancerPsychologyEnvironmental health

Abstract

fetched live from OpenAlex

With improved average longevity, the issue of polypathology in the cancer population is of growing importance, because it will increasingly affect more people. The present study piloted two self-report surveys aiming to provide preliminary data on the nature of polypathology and supportive care needs (met and unmet) of cancer survivors. Survivors were recruited from outpatient clinics at the Princess Margaret Hospital in Toronto and were asked to complete and give feedback on the surveys. Of a convenience sample of 88 survivors, almost three quarters (73%) reported having polypathology, and 64% had at least 1 unmet need. Results also suggest that those with the highest number of needs were more likely to have polypathology. Our study invites further assessments with self-report surveys of the complex picture that arises when cancer is not the only disease affecting a person. It also highlights the need for innovative supportive services to address patient needs.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.112
GPT teacher head0.383
Teacher spread0.271 · 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 designQualitative
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

Citations6
Published2013
Admission routes4
Has abstractyes

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