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Record W2788397352 · doi:10.1136/bmjopen-2017-017578

Blood-based biomarkers of cancer-related cognitive impairment in non-central nervous system cancer: protocol for a scoping review

2018· review· en· W2788397352 on OpenAlexafffund
Samantha Mayo, John Kuruvilla, Rob C. Laister, Ana Patricia Ayala, Mark W Alm, Will Byker, Debra Lynch Kelly, Leorey N. Saligan

Bibliographic record

VenueBMJ Open · 2018
Typereview
Languageen
FieldMedicine
TopicCancer-related cognitive impairment studies
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
FundersUniversity of Toronto
KeywordsMedicineCognitive impairmentCancerProtocol (science)CognitionCentral nervous systemNeurologyBioinformaticsPathologyAlternative medicineInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

How this classification was reachedexpand

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Protocol
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewlow
gptno category
Domain: not available · Genre: Protocol
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.090
metaresearch head score (Gemma)0.111
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.090
Threshold uncertainty score0.478

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0900.111
Meta-epidemiology (narrow)0.0050.004
Meta-epidemiology (broad)0.0110.016
Bibliometrics0.0140.014
Science and technology studies0.0040.004
Scholarly communication0.0080.008
Open science0.0050.007
Research integrity0.0090.006
Insufficient payload (model declined to judge)0.0680.013

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 head0.130
GPT teacher head0.509
Teacher spread0.380 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreProtocol

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

Citations7
Published2018
Admission routes2
Has abstractyes

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