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Record W2729702488 · doi:10.3747/co.24.3432

Resource Utilization and Costs of Managing Patients with Advanced Melanoma: A Canadian Population-Based Study

2017· article· en· W2729702488 on OpenAlexafffundvenueabout
Femida Gwadry‐Sridhar, Soodeh Nikan, Ali Hamou, Soo Jin Seung, Teresa M. Petrella, Anthony M. Joshua, Scott Ernst, Nicole Mittmann

Bibliographic record

VenueCurrent Oncology · 2017
Typearticle
Languageen
FieldMedicine
TopicCutaneous Melanoma Detection and Management
Canadian institutionsPrincess Margaret Cancer CentreHealth Sciences CentreCancer Care OntarioSunnybrook Health Science CentreWestern University
FundersInstitute for Clinical Evaluative Sciences
KeywordsMedicinePopulationResource (disambiguation)Intensive care medicineData scienceEnvironmental healthComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: The use and detailed costs of services provided for people with advanced melanoma (amel) are not well known. We conducted an analysis to determine the use of health care services and the associated costs delineated by relevant attributable costs, which we defined for subjects in the province of Ontario. METHODS: (9th revision, Clinical Modification) 172 codes and histology codes was identified. A cohort of individuals with amel having a combination of at least 1 palliative, 1 medical oncology, and 1 hospitalization code was generated. The health system services used by this population were clustered into hospitalization, palliation, physician medical visits, medication, homecare, laboratory, diagnostics, and other resources. Overall rates of use and disaggregated costs were determined by phase of care for the entire cohort. RESULTS: The mean age for the 2748 individuals in the cohort was 67 years. The greater proportion of the patients were men (65.6%) and were more than 65 years of age (>50%). In this advanced cohort, fewer than 45% of patients were alive 3 years after the malignant melanoma diagnosis. The average annual cost per patient over the time horizon was $6,551. At $15,830, year 1 after diagnosis was the most expensive, followed by year 2, at $8,166. CONCLUSIONS: Our data provide a baseline for the costs associated with amel treatment. Future studies will include newer agents and comparative effectiveness research for personalized therapies.

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 score0.279
Threshold uncertainty score0.954

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 head0.048
GPT teacher head0.349
Teacher spread0.301 · 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

Citations11
Published2017
Admission routes4
Has abstractyes

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