MétaCan
Menu
Back to cohort
Record W2097370514 · doi:10.1093/annonc/mdu245

Still a long way to go to achieve multidisciplinarity for the benefit of patients: commentary on the ESMO position paper (Annals of Oncology 25(1): 9–15, 2014)

2014· letter· en· W2097370514 on OpenAlexaff
Vincenzo Valentini, Per‐Anders Abrahamsson, Sanchia Aranda, A. Astier, Riccardo A. Audisio, Mathieu Boniol, L. Bonomo, Alessandro Brunelli, Barry D. Bultz, Arturo Chiti, Francesco De Lorenzo, Jesper Grau Eriksen, Vicky Goh, Mary Gospodarowicz, Luigi Grassi, Joan Kelly, Rolf‐Dieter Kortmann, Tezer Kutluk, Ananda Plate, Graeme J. Poston, Tiina Saarto, Riccardo Soffietti, A. Torresin, Wim H. van Harten, J. Fred Verzijlbergen, Christof von Kalle, Philip Poortmans

Bibliographic record

VenueAnnals of Oncology · 2014
Typeletter
Languageen
FieldMedicine
TopicAdvances in Oncology and Radiotherapy
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity of Calgary
Fundersnot available
KeywordsMedicineMultidisciplinary approachOncologyInternal medicinePosition paperAnnalsMedical educationFamily medicinePathology

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)0.009
metaresearch head score (Gemma)0.062
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.136
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.062
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0080.007
Scholarly communication0.0070.009
Open science0.0040.005
Research integrity0.1360.106
Insufficient payload (model declined to judge)0.0090.007

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 head0.059
GPT teacher head0.424
Teacher spread0.364 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2014
Admission routes1
Has abstractno

Explore more

Same venueAnnals of OncologySame topicAdvances in Oncology and RadiotherapyFrench-language works237,207