MétaCan
Menu
Back to cohort
Record W4405399771 · doi:10.1177/17455057241306809

Guidelines unmet: Assessing gaps in breast cancer survivorship care

2024· article· en· W4405399771 on OpenAlexaffabout
Dana Pearl, Anna N. Wilkinson

Bibliographic record

VenueWomen s Health · 2024
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSurvivorship curveMedicineConcordanceBreast cancerHealth careCancer survivorshipFamily medicineCancerPsychological interventionMedical recordNursingInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: In Canada, after completing their treatment at oncology centers in tertiary care facilities, most breast cancer patients are discharged and receive survivorship care from primary care providers (PCPs). Evidence-based guidelines exist to inform appropriate care for breast cancer survivor follow-up. OBJECTIVES: This study analyzed the concordance of breast cancer survivorship follow-up care by PCPs with recommended guidelines at an academic Family Health Team (FHT) in Ottawa. DESIGN: Retrospective chart review of electronic medical records of rostered patients from FHT. METHODS: Data was extracted from the charts of 60 breast cancer survivors. Concordance of breast cancer survivorship care by PCPs with evidence-based guidelines was established in three key survivorship domains: surveillance for recurrence or new cancers, management of treatment side effects and preventative health. RESULTS: PCPs provide care concordant with guidelines only 20% of the time, with areas such as preventative care at 86.7% concordance far better than management of side effects at 58.3% and oncological surveillance at 38.3%. Care did not significantly differ by age at diagnosis. CONCLUSION: These results highlight gaps in the current survivorship care delivery and function as a baseline for comparative analyses for future interventions to optimize survivorship follow-up care.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.522
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.042
GPT teacher head0.401
Teacher spread0.359 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueWomen s HealthSame topicCancer survivorship and careFrench-language works237,207