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Record W4400453391 · doi:10.1136/bmjebm-2024-sdc.255

256 Triage decision tools for cancer related symptoms

2024· article· en· W4400453391 on OpenAlexaffabout
Dawn Stacey, C Laire Ludwig, Gail Macartney, Joy Tarasuk, Craig Kuziemsky, Meg Carley

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsCancer Care Nova ScotiaMacEwan UniversityUniversity of Prince Edward IslandOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsTriageCancerComputer scienceMedical emergencyMedicineInternal medicine

Abstract

fetched live from OpenAlex

Introduction Symptoms can quickly become life-threatening for people receiving cancer treatments; thus, high quality symptom assessment and decision triage tools are critical for keeping patients safe. We co- produced the pan-Canadian Oncology Symptom Triage and Remote Support (COSTaRS) practice guides, a set of evidence-informed symptom triage decision tools used in outpatient cancer programs across Canada. Our current research aimed to: a) update the evidence in 17 COSTaRS practice guides; and b) evaluate the model of co-production. Methods We are co-producing a 2-year study with an expanded team of researchers, patient, caregiver, nurses, managers, policy makers and collaborators (e.g., CANO/ACIO, Cancer Care Ontario, de Souza Institute) who interact with and influence all levels of the Canadian healthcare system. We co- conducted 17 systematic reviews guided by Cochrane methods and reported using PRISMA. Eligible citations were clinical practice guidelines and systematic reviews with meta-analysis. We evaluated our co-production approach using the Patient Engagement in Research (PEIRS-22) instrument. Results We identified 124 guidelines and 65 systematic reviews. Preliminary findings showed that each of the 17 symptom guides had a mean of 19 new citations to be included (range 9 to 38). The 17 symptom guides were updated. There were few changes based on new evidence. Drafts of the 17 updated symptom guides will be validated by oncology nurses from across Canada. To facilitate co-production, monthly updates were sent to the team with invitations to participate on research activities in process. Discussion Although synthesized evidence on symptom management is continuing to emerge, decision triage tools required few changes. Co-production of decision triage tools is possible. Conclusion(s) Nurses and other clinicians can use these updated triage decision tools for supporting patients reporting cancer symptoms. They are publicly available (English, French) free of charge at T elephone Guidelines CANO/ACIO (cano-acio.ca) and https://ktcanada.ohri.ca/costars.

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)0.055
metaresearch head score (Gemma)0.262
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: Methods · Consensus signal: none
Teacher disagreement score0.128
Threshold uncertainty score0.292

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0550.262
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0150.016
Science and technology studies0.0020.001
Scholarly communication0.0050.003
Open science0.0040.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.107
GPT teacher head0.503
Teacher spread0.396 · 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
GenreMethods

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

Citations0
Published2024
Admission routes2
Has abstractyes

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