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Record W4410015421 · doi:10.1016/j.jgo.2025.102238

Strengthening interprofessional and interdisciplinary collaboration in geriatric oncology initiatives: An adapted World Café at the 2024 International Society of Geriatric Oncology (SIOG) Annual Conference, a joint initiative of the SIOG Nursing, Allied Health, and Scientists Interest Group and the Canadian Association of Nurses in Oncology (CANO) Oncology and Aging Special Interest Group

2025· article· en· W4410015421 on OpenAlexafffundabout
Fay J. Strohschein, Sophie Pilleron, Manon Lemonde, Fay Bennie, Kristen R. Haase, Michelle Hannan, Caitríona Barrett, Cassandra Vonnes, Susie Monginot, V. Slavova-Boneva, Martine Puts

Bibliographic record

VenueJournal of Geriatric Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsPrincess Margaret Cancer CentreCanadian Centre for Applied Research in Cancer ControlOntario Tech UniversityUniversity of Calgary
FundersCanadian Institutes of Health ResearchFonds National de la Recherche LuxembourgLouisiana State UniversityHealth Research
KeywordsGeriatric oncologyMedicineNursingFamily medicineOncologyInternal medicineCancer

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.019
metaresearch head score (Gemma)0.014
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: none
Teacher disagreement score0.992
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.014
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0010.001
Science and technology studies0.0170.003
Scholarly communication0.0090.010
Open science0.0040.026
Research integrity0.0100.017
Insufficient payload (model declined to judge)0.0170.004

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 head0.027
GPT teacher head0.373
Teacher spread0.346 · 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

Citations1
Published2025
Admission routes3
Has abstractno

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