MétaCan
Menu
Back to cohort
Record W2109068461 · doi:10.1093/intqhc/mzt003

Timeliness of cancer care from diagnosis to treatment: a comparison between patients with breast, colon, rectal or lung cancer

2013· article· en· W2109068461 on OpenAlexafffundabout
Xue Li, Andrew Scarfe, Karen King, David Fenton, Charles Butts, Marcy Winget

Bibliographic record

VenueInternational Journal for Quality in Health Care · 2013
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsUniversity of AlbertaAlberta Health Services
FundersAlberta Cancer Foundation
KeywordsMedicineColorectal cancerBreast cancerCancerCancer registryMedical recordLung cancerStage (stratigraphy)PopulationRetrospective cohort studyHealth careInternal medicineOncologyEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this study was to assess the value in measuring specific time intervals across cancer sites to identify potentially important variation in the timeliness of cancer care that may inform needed changes and/or improvements in coordination of care. DESIGN: Retrospective population-level study. Demographic and treatment information were obtained from the Alberta Cancer Registry. Date of oncologist-consult was obtained from cancer medical records. SETTING: Alberta, Canada. PARTICIPANTS: All patients diagnosed in 2005 with breast, colon, rectal or lung cancer who were residents of Alberta, Canada. MAIN OUTCOME MEASURES: (i) Number of days from diagnosis to first treatment by treatment modality and cancer site, (ii) number of days from surgery to post-surgery consultation and subsequent treatment and (iii) relationship between clinical and demographic factors and the cancer-specific provincial median time for outcome measures (i) and (ii). RESULTS: Time from diagnosis to surgery, if first treatment, was ∼4 months for lung cancer compared with 1-2 months for breast and colorectal cancers. Factors associated with this time interval for breast and colorectal cancers was stage at diagnosis but was region of residence for lung cancer. CONCLUSIONS: Important variation within and across cancer sites identified in the care intervals evaluated in this study provides relevant information to inform local areas for improvement. Comparisons of these intervals across healthcare systems may also provide insights into strengths of different models for coordinating 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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.107
GPT teacher head0.507
Teacher spread0.400 · 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

Citations37
Published2013
Admission routes3
Has abstractyes

Explore more

Same venueInternational Journal for Quality in Health CareSame topicGlobal Cancer Incidence and ScreeningFrench-language works237,207