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Record W2805627830 · doi:10.1111/codi.14086

Session 3: Boosting primary and recurrent rectal cancer: how far can we push the radiotherapy envelope?

2018· review· en· W2805627830 on OpenAlexaff
Svetlana Balyasnikova, T. Vuong, Anita Wale, Irene Chong, H.J.T. Rutten, Gina Brown

Bibliographic record

VenueColorectal Disease · 2018
Typereview
Languageen
FieldMedicine
TopicColorectal Cancer Surgical Treatments
Canadian institutionsJewish General Hospital
FundersNational Institute for Health and Care Research
KeywordsMedicineRadiation therapySession (web analytics)Colorectal cancerBoosting (machine learning)Medical physicsOncologyCancerSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Neoadjuvant pelvic radiotherapy is widely used for patients with advanced rectal cancer. The trade-off between dose and response is well-established, yet little consensus remains on the precise methods of delivery and doses given in different scenarios. Professor Vuong reviews the evidence base and trial evidence on the escalation of radiotherapy dose and the methods of achieving this.

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)0.003
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.028
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0280.005

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 head0.048
GPT teacher head0.344
Teacher spread0.295 · 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
GenreReview

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
Published2018
Admission routes1
Has abstractyes

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