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Record W4402558718 · doi:10.1016/j.annonc.2024.08.2121

1902P Modified Delphi consensus on interventions for radiation dermatitis in breast cancer: A Canadian expert perspective

2024· article· en· W4402558718 on OpenAlexaffabout
Tarek Hijal, Hannah Dahn, Valérie Théberge, Valérie Panet-Raymond, Ericka Wiebe, Iwa Kong, Danielle Rodin, Natalie Logie, Edward Chow, Babar Bashir, Philip Wright, J-M Caudrelier, J-M. Bourque, Michael Yassa, Michael Sauder, Joël Claveau, Ciudad Bolívar, S. Marchuk, Nour R. Dayeh, Jeffrey Cao

Bibliographic record

VenueAnnals of Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicAdvances in Oncology and Radiotherapy
Canadian institutionsHôtel-Dieu de QuébecHôpital Maisonneuve-RosemontUniversity Health NetworkCancerCare ManitobaHealth Sciences CentreOttawa HospitalPrincess Margaret Cancer CentreUniversity of British ColumbiaSunnybrook Health Science CentreMcGill University Health CentreUniversity of British Columbia HospitalUniversity of SaskatchewanCentre Hospitalier de l’Université de MontréalUniversité LavalDalhousie University
Fundersnot available
KeywordsMedicinePsychological interventionPerspective (graphical)DelphiBreast cancerFamily medicineDelphi methodMedical physicsDermatologyCancerInternal medicineNursingArtificial intelligence

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)0.144
metaresearch head score (Gemma)0.217
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 score0.197
Threshold uncertainty score0.761

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1440.217
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.005
Science and technology studies0.0120.008
Scholarly communication0.0070.004
Open science0.0050.009
Research integrity0.0090.011
Insufficient payload (model declined to judge)0.0120.001

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 head0.094
GPT teacher head0.498
Teacher spread0.404 · 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

Citations0
Published2024
Admission routes2
Has abstractno

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