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Record W2164073998 · doi:10.1200/jop.2014.001390

Population-Based Standardized Symptom Screening: Cancer Care Ontario's Edmonton Symptom Assessment System and Performance Status Initiatives

2014· article· en· W2164073998 on OpenAlexaffabout
José Pereira, Esther Green, Sean Molloy, Deborah Dudgeon, Doris Howell, Monika K. Krzyzanowska, Wenonah Mahase, Reena Tabing, Sara Urowitz, Laura MacDougall

Bibliographic record

VenueJournal of Oncology Practice · 2014
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsCancer Care OntarioUniversity of TorontoUniversity of OttawaQueen's UniversityUniversity Health Network
Fundersnot available
KeywordsMedicineInteractive kioskCancerMEDLINEFamily medicinePopulationEnvironmental healthWorld Wide WebInternal medicine

Abstract

fetched live from OpenAlex

To improve symptom management, the Ontario Cancer Symptom Management Collaborative uses common assessment and care management tools including Interactive Symptom Assessment and Collection, which allows patients to report their symptoms via kiosks or tablets directly to clinicians in real-time.

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)0.007
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.967
Threshold uncertainty score0.237

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.018
GPT teacher head0.357
Teacher spread0.339 · 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 designObservational
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

Citations68
Published2014
Admission routes2
Has abstractyes

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