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Engagement in a statewide oral oncolytic collaborative and practice impact.

2016· article· en· W2589269013 on OpenAlexaboutno aff
Jane Alcyne Severson, Emily Mackler, Grayce Galiyas, Laura Petersen, Jamie Lindsay, Teresa M. Salgado, Emily Jane Davis, Karen B. Farris

Bibliographic record

VenueJournal of Clinical Oncology · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDocumentationConcordanceBest practiceWorkflowScale (ratio)Quality managementSoftware deploymentOncolytic virusMedical educationManagement systemInternal medicineCancerDatabaseOperations management

Abstract

fetched live from OpenAlex

89 Background: The rapid shift to oral oncolytic therapy presents challenges to oncology practitioners. The purpose of this study is to describe how participation in a statewide oral oncolytic improvement collaborative where best practices and resources were shared can readily impact quality of care as measured by national standards. Methods: The Michigan Oncology Quality Consortium (MOQC) hosted a series of learning collaborative sessions focused on topics and deployment of resources specific to oral oncolytic management and quality improvement. Participating practices performed pre/post self-assessments in Oct. 2013 and Apr. 2015 (n = 3). Concordance with national ASCO QOPI and ONS standards was compared, including documentation (5 measures), patient education (7 measures), and follow-up/monitoring (4 measures). A response scale of always, sometimes, and never was used. Specifically, practices were surveyed to assess which evidence-based MOQC resources were implemented: patient intake template, drug-specific self-management guides, start date mailer, medication calendar, primary care physician communication template, Edmonton Symptom Assessment Scale (ESAS), and patient adherence questionnaires. Results: Practice A showed improvement in documentation, patient education, and monitoring. This practice used the initial oral chemotherapy template, ESAS, patient education templates, and patient calendar (Table). Practice B implemented 6 resources and demonstrated improvements in 15 metrics. Practice C implemented 4 resources, namely patient-focused resources, to improve all patient education and monitoring metrics. Conclusions: Use of the collaborative model and supplying oncology teams with scientific evidence, standard workflows, and resources improves concordance with national standards of care. Large-scale deployment of this model program may provide a clinically efficient and effective mechanism to enhance widespread change. [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)0.020
metaresearch head score (Gemma)0.042
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 score0.023
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.042
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0040.002
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.189
GPT teacher head0.470
Teacher spread0.281 · 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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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