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Patients as partners in managing cancer treatment-related toxicity.

2016· article· en· W2590248786 on OpenAlexaffabout
Erin Redwood, Kathy Vu, M. Wright, Colleen Fox, Noor Ani Ahmad, Vicky Simanovski, Leonard Kaizer, Monika K. Krzyzanowska

Bibliographic record

VenueJournal of Clinical Oncology · 2016
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsPrincess Margaret Cancer CentreSt. Michael's HospitalCancer Care Ontario
Fundersnot available
KeywordsStakeholder engagementMedicineGovernment (linguistics)StakeholderAgency (philosophy)Quality managementQuality (philosophy)Public relationsNursingBusinessMarketingPolitical scienceService (business)

Abstract

fetched live from OpenAlex

215 Background: Cancer Care Ontario is the government agency responsible for improving cancer services across Ontario’s 14 regions. To promote advances in best practices related to systemic treatment, Cancer Care Ontario hosts annual Systemic Treatment Safety Symposia, which serve as a platform to discuss quality and safety issues and results of quality improvement (QI) initiatives in alignment with the Systemic Treatment Provincial Plan. For the first time, the 2015 Symposium brought together patients and providers to discuss gaps and opportunities for improvement in relation to toxicity management (TM) during chemotherapy. The main goal of the event was to define improvement priorities. Methods: The Symposium presents a valuable engagement opportunity with regional stakeholders including medical oncologists, nurses, pharmacists, administrators and patient and family advisors. An interactive agenda was designed to elicit direction from these stakeholders. At the event attendees identified and prioritized improvement opportunities using a simulated investment scenario, where marked bills were given to participants to finance the solutions they felt would best address the challenges posed by current TM. The mock money was counted and analyzed based on the role of the ‘investors’ and the prioritized theme. Results: The Symposium had 92 attendees including 17 patients and caregivers. Themes that emerged are presented in the Table below. Endorsement varied depending on stakeholder group. For example, Access was the top improvement priority for patients, whereas Communication was highest for providers. Conclusions: A one day engagement event that brings together patients and providers can be successful in identifying priority areas for quality improvement. Based on the outcomes of the prioritization exercise, improving access to oncology providers for TM 24/7 was identified as a focus area for provincial and regional QI initiatives. [Table: see text]

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)0.008
metaresearch head score (Gemma)0.017
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0090.002
Scholarly communication0.0060.004
Open science0.0010.010
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0260.003

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 head0.370
GPT teacher head0.635
Teacher spread0.265 · 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
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".

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Citations0
Published2016
Admission routes2
Has abstractyes

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