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Record W2892856662 · doi:10.1200/jgo.18.21600

Using Standardized Electronic Pathology and Surgery Data to Inform Clinical Quality Improvement and Health System Planning

2018· article· en· W2892856662 on OpenAlexaffabout
Shaalee Sone, Jessica Kitchen, Shaheena Mukhi, Mary Argent-Katwala, John R. Srigley

Bibliographic record

VenueJournal of Global Oncology · 2018
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsCanadian Partnership Against Cancer
Fundersnot available
KeywordsMedicinePsychological interventionHealth careFamily medicineDescriptive statisticsColorectal cancerMEDLINECancerNursingInternal medicine

Abstract

fetched live from OpenAlex

Background: Practice variation in diagnosis and treatment exists between clinicians and jurisdictions across Canada. This variation can impact the quality of care that patients receive and patient outcomes. Knowledge of the scale and type of variation is the first step to developing action plans to improve consistency and enhance patient care. Aim: We aimed to establish a method by which to examine the magnitude of practice variation between clinicians and interjurisdictionally within the cancer system. We leveraged and derived evidence from discrete pathology data collected by five Canadian jurisdictions at the point of care to identify areas to improve quality of cancer care services and to direct patient care. Methods: Fifty pathologists, surgeons, and medical oncologists from 10 jurisdictions conferred to leverage literature and data standards (developed by the College of American Pathologists (CAP)) to create 48 descriptive and outcome indicators related to five cancers: breast, lung, colorectal, endometrial, and prostate cancer. Five jurisdictions collected and used data to generate the indicators. This baseline data were reviewed by 65 clinicians. Results: Interjurisdictional comparative baseline data analyses on 48 indicators showed clinical validity and relevance for use to direct downstream patient care. Data characterizing cancer type, stage, and grade distribution were consistently reported across geography and aligned with the evidence noted in the literature. The data also noted practice and performance variation across multiple cancer sites. For example, although the recommended guideline is to examine at least 12 lymph nodes in 90% of colorectal cancer patients, only one province met this target. Another example is Lynch syndrome testing, which may be important for patients with a diagnosis of colorectal or endometrial cancer depending on the age at diagnosis and family history. The data showed that 0%–70% of patients diagnosed with colorectal cancer prior to age 70 received testing for Lynch syndrome, and only 10%–40% of endometrial cancer cases were tested for markers of Lynch syndrome across the country. The value of these indicators is enormous to inform potential training opportunities and set standards of care at the local or broader clinical governance level so that consistent, high-quality care is delivered in accordance with evidence-based guidelines. Conclusion: Practice variation exists between clinicians and jurisdictions, and comparative pathology data can be used to create a cancer learning system. Four jurisdictions are now embarking on leveraging indicator data analysis to generate physician-level feedback reports and convening communities of practice with the goal of facilitating peer-to-peer conversations, and establishing benchmarks and targets to improve the quality of care, refine or develop clinical guidelines, and inform health system planning in Canada. These lessons can be applied in other cancer systems.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation 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 score0.462
Threshold uncertainty score0.673

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.615
GPT teacher head0.657
Teacher spread0.042 · 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 teacher head, 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

Citations0
Published2018
Admission routes2
Has abstractyes

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