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Record W2147066595 · doi:10.1093/jncimonographs/lgu042

Advancing the Evidence Base and Transforming Cancer Care Through Interprofessional Collegiality: The Society for Integrative Oncology

2014· article· en· W2147066595 on OpenAlexaffabout
Susan Bauer‐Wu, Suzanna M. Zick, Richard T. Lee, Lynda G. Balneaves, Heather Greenlee

Bibliographic record

VenueJNCI Monographs · 2014
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineModalitiesHealth careOncologyIntegrative medicineAlternative medicineInternal medicineMedical educationFamily medicinePathology

Abstract

fetched live from OpenAlex

The Society for Integrative Oncology (SIO) is pleased to support this seminal issue of JNCI Monographs devoted to integrative oncology and cancer survivorship. The SIO is an international professional society whose mission is to advance evidence-based, comprehensive integrative health care to improve the lives of people affected by cancer (www.integrativeonc.org). Since its founding in 2003 by pioneers in the field—Drs Barrie Cassileth, David Rosenthal, and Lorenzo Cohen—SIO has consistently encouraged rigorous scientific evaluation of both pre-clinical and clinical science, while advocating for the transformation of oncology care to integrate evidence-based complementary approaches. The vision of SIO is to have research inform the true integration of complementary modalities into oncology care, so that evidence-based complementary care is accessible and part of standard cancer care for all patients across the cancer continuum. As an interdisciplinary and interprofessional society, SIO is uniquely poised to lead the “bench to bedside” efforts in integrative cancer care. SIO members are comprised of a variety of professionals including, but not limited to, conventional cancer clinicians (ie, medical oncologists, radiation oncologists, and oncology nurses), family medicine providers, naturopathic doctors, traditional Chinese medicine practitioners, mind–body therapists, nutritionists, patient advocates, and basic scientists. This diverse membership facilitates true clinical integration of complementary modalities because such change requires breadth of expertise and different perspectives, to listen and learn from one another while challenging each other’s assumptions and existing paradigms. Annual SIO conferences, cosponsored with major academic centers, provide an unparalleled interprofessional opportunity for clinicians, researchers, and patient advocates to come together and advance integrative oncology research and best clinical practices. Health-care providers and patients alike are often searching for the next best treatment for cancer and ways to improve their quality of life in the midst of the challenges posed by disease or treatment side effects. Providers and patients wade through hundreds of articles and internet stories trying to find reliable information, which generally leaves them feeling overwhelmed and not sure what to believe. SIO’s commitment to research and knowledge translation has resulted in the development of three state-of-the-science papers and clinical guidelines. In 2009, the first article provided evidence-based clinical practice guidelines in complementary therapies with a focus on botanicals (1). Then the second article, published in 2013 in Chest, focused on integrative medicine in lung cancer (2). Finally, published in this monograph, SIO’s most recent guidelines focus on breast cancer survivorship and the use of integrative therapies in supportive care. This extensive research review and synthesis will be an invaluable resource to clinicians: to help them be well prepared to care for women with breast cancer, make appropriate referrals and recommendations, and answer breast cancer patients’ questions about the use of complementary therapies. In parallel, the guidelines will provide important information to women with breast cancer as they face decisions on whether and how to incorporate integrative therapies into their self-care.

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.452
Threshold uncertainty score0.496

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.056
GPT teacher head0.414
Teacher spread0.358 · 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 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

Citations2
Published2014
Admission routes2
Has abstractyes

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