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Record W1971462664 · doi:10.3747/co.v15i4.288

Healing and Survivorship: What Makes a Difference?

2008· article· en· W1971462664 on OpenAlexaffvenue
Hillel D. Braude, Neil Macdonald, Martin Chasen

Bibliographic record

VenueCurrent Oncology · 2008
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsSurvivorship curveMedicineBioinformaticsCancerBiologyInternal medicine

Abstract

fetched live from OpenAlex

Literature demonstrating the importance of social relationships for cancer survivorship is accumulating. Building on that literature, the term "Healing Ties" refers to the scientific and popular factors supporting the idea that relationships and community are essential for healing. However, difficulties arise in assessing the effect of social support for survivorship.The current paper reviews the role in survivorship of social support, with respect to the explanatory model provided by neuro-oncology and psycho-neuro-immunology. Taking cognizance of the importance of social relationships, the model of cancer rehabilitation aims, through its interdisciplinary framework, to restore a sense of well-being and to facilitate healing by optimizing the capability for full social relationships and engagement with the world.

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)0.010
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.025
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0040.017
Scholarly communication0.0110.019
Open science0.0020.005
Research integrity0.0090.010
Insufficient payload (model declined to judge)0.0060.001

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 head0.148
GPT teacher head0.389
Teacher spread0.241 · 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 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

Citations13
Published2008
Admission routes2
Has abstractyes

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