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Record W4399977213 · doi:10.1016/j.lansea.2024.100441

Mobilizing students to effect multidisciplinary cancer care: the Tumor Board Establishment Facilitation Forum

2024· article· en· W4399977213 on OpenAlexaff
Muhammad Abdul Rehman, Urooba Jawwad, Erfa Tahir, Unaiza Naeem, Maheen Qamar, Nowal Hussain, Nimrata Kumari, Ahmed Nadeem Abbası, Agha Muhammad Hammad Khan

Bibliographic record

VenueThe Lancet Regional Health - Southeast Asia · 2024
Typearticle
Languageen
FieldMedicine
TopicAdvances in Oncology and Radiotherapy
Canadian institutionsMcGill University
FundersTurbine Engine Fatigue Facility
KeywordsMultidisciplinary approachFacilitationMedicineMedical educationPsychologyPolitical scienceNeuroscience

Abstract

fetched live from OpenAlex

In 1995, chief medical officers, Dr. Calman and Dr. Hine established multidisciplinary tumor boards (MTB) as a standard of care in the United Kingdom.1 Almost 30 years later, it is a fundamental phenomenon in cancer care. However, it is not implausible to discern that not all cancer cases are discussed in MTBs. In the face of an increasing influx of new cases, the demand is extremely high for MTBs. In 2022, there were 9,800,000 new cancer cases in Asia.2 Yet, even nationwide MTBs only discuss cases in the hundreds.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.472

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 integrity0.0000.001
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 head0.034
GPT teacher head0.444
Teacher spread0.410 · 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 designNot applicable
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

Citations3
Published2024
Admission routes1
Has abstractyes

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