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Record W7047318600

Examining the pathway to diagnosis and treatment of lymphoma in Manitoba: patient and system factors resulting in delay

2016· dissertation· en· W7047318600 on OpenAlexaffabout

Bibliographic record

VenueMspace (University of Manitoba) · 2016
Typedissertation
Languageen
FieldEngineering
TopicSuperconducting Materials and Applications
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsChartMedical diagnosisLymphomaTimelineDiagnosis codeCancerHodgkin lymphomaCancer treatment
DOInot available

Abstract

fetched live from OpenAlex

The province of Manitoba has set a goal of reducing time from suspicion of cancer to treatment to a target of sixty days. To attain this goal, a baseline understanding of current time intervals is required. This study examined system, diagnostic and treatment delay in adult patients (> 17) diagnosed with Lymphomas from 2005 to 2010 using administrative data (Manitoba Cancer Registry, Manitoba Health billing data and Hospital Abstract data) and a chart review of a random subset of patients. A triangulated data approach was used to identify suspicion of lymphoma and milestones in the patient journey and to measure delays in diagnosis and treatment. Using an iterative consultative process, an algorithm was built to identify index events likely related to subsequent lymphoma diagnosis. Then, claims data was searched for a referring provider for each index event. The last visit with a referring provider, prior to the first index event, was selected as date of high suspicion. The study found that 14.8% of patients met the provincial target of less than sixty days from suspicion to treatment. Median time from high suspicion to treatment, referred to as system delay, was 128 days and the median time from diagnosis to treatment was 41 days. Time to diagnosis accounted for two thirds of system delay. In conclusion, this study demonstrated the merit of a triangulated approach. As well the clinical pathway developed and the target timelines for milestones have operational value and can be used to direct process improvements to shorten delays for future patients.

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 score0.237
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.026
GPT teacher head0.191
Teacher spread0.165 · 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
Published2016
Admission routes2
Has abstractyes

Explore more

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