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Record W3211055731 · doi:10.3390/curroncol28060375

Acute Care Use by Breast Cancer Patients on Adjuvant Chemotherapy in Alberta: Demonstrating the Importance of Measurement to Improving Quality

2021· article· en· W3211055731 on OpenAlexaffvenueabout
Che Hsuan David Wu, May Lynn Quan, Shiying Kong, Yuan Xu, Jeffrey Cao, Sasha Lupichuk, Lisa Barbera

Bibliographic record

VenueCurrent Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicNeutropenia and Cancer Infections
Canadian institutionsAlberta Health ServicesUniversity of Calgary
Fundersnot available
KeywordsMedicineBreast cancerIncidence (geometry)Emergency departmentAdjuvant chemotherapyLogistic regressionChemotherapyBreast cancer chemotherapyRetrospective cohort studyCancerInternal medicineCohortPopulationEmergency medicineEnvironmental healthNursing

Abstract

fetched live from OpenAlex

Breast cancer patients receiving adjuvant chemotherapy are at increased risk of acute care use. The incidence of emergency department (ED) visits and hospitalizations (H) have been characterized in other provinces but never in Alberta. We conducted a retrospective population-based cohort study using administrative data of women with stage I-III breast cancer receiving adjuvant chemotherapy. Rates of ED and H use in the 180 days following chemotherapy initiation were determined, and logistic regression was performed to identify risk factors. We found that 47% of women receiving adjuvant chemotherapy experienced ED or H, which compared favourably to other provinces. However, Alberta had the highest rate of febrile neutropenia-related ED visits, and among the highest chemotherapy-related ED visits. The incidence of acute care use increased over time, and there were significant institutional differences despite operating under a single provincial healthcare system. Our study demonstrates the need for systematic measurement and the importance of quality improvement programs to address this gap.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.050
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.081
GPT teacher head0.407
Teacher spread0.326 · 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

Citations4
Published2021
Admission routes3
Has abstractyes

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