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P1-11-11: Wait Times for Breast Cancer Care in Manitoba 2009–2010. Time To Face the Challenge.

2011· article· en· W2324428487 on OpenAlexaffabout
Tara Carpenter-Kellett, Maged N. F. Nashed

Bibliographic record

VenueCancer Research · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsCancerCare Manitoba
Fundersnot available
KeywordsMedicineBreast cancerCancerPopulationRadiation therapyDiseasePresentation (obstetrics)PediatricsSurgeryGeneral surgeryInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background: Wait times for patients with breast cancer to receive oncological treatment vary significantly. In Manitoba, the radiation treatment wait times improved in the year 2005 compared to 2001. However, other parts of the patient's journey have lengthened and negated the reduction seen in radiation treatment wait times. Aims: To examine the overall time from disease suspicion to treatment of breast cancer patients from June 2009 to June 2010 and to compare to the previously published wait times. Methods: This population-based retrospective study looked at representative samples of women newly diagnosed with breast cancer. Patients were followed from the time of first presentation, either to their family physician or after a suspicious screening mammogram, to the time adjuvant treatment was started. Each patient's journey was subdivided into different chronological stages. The data was compared to the wait times reported in 2005. Results: 363 patients’ data was collected and analyzed. The median elapsed time in days for each phase of the journey was calculated. Total wait times from suspicion to diagnosis were also calculated. The wait times of most stages of the patients’ journey have worsened when compared to 2005. However, there was some improvement in the diagnostic part of the journey. Delays to surgery and pathology have contributed to lengthening of the total journey. There was also a systematic and repetitive administration delay at each transition. Screen-detected cases had a shorter journey than those who presented through a family physician. Conclusions: In spite of improvements achieved in the wait times at some stages of the journey, the total length of the patient journey has not been shortened. This represents not only individual team failures but also a built in complexity in the health system. In order to achieve a meaningful cut in wait times for breast cancer patients, the entire trajectory of care needs to be addressed. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P1-11-11.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.230
Threshold uncertainty score0.462

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.131
GPT teacher head0.326
Teacher spread0.194 · 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
Published2011
Admission routes2
Has abstractyes

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