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Record W2778137650 · doi:10.1177/0840470417722568

Evolution of cancer treatment and evolving challenges

2017· review· en· W2778137650 on OpenAlexaboutno aff
Lynne Nakashima

Bibliographic record

VenueHealthcare Management Forum · 2017
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsnot available
Fundersnot available
KeywordsAllianceNegotiationHealthcare systemConsistency (knowledge bases)MedicineCancer treatmentProcess (computing)Cancer drugsHealth careBusinessCancerPolitical scienceComputer science

Abstract

fetched live from OpenAlex

This article will provide an overview of the evolving nature of cancer treatment, the benefits and challenges of these new treatments, and the leadership strategies required to manage the evolution from a system perspective. The number and complexity of novel cancer therapies, while offering improved patient outcomes, has become a challenge for the healthcare system due to the high cost of these new therapies. In Canada, the implementation of the pan-Canadian Oncology Drug Review and the pan-Canadian Pharmaceutical Alliance helps to streamline the review and negotiating process and to ensure consistency across provinces. Strategies to support these processes include ensuring safe patient treatment, patient counselling, clinician education, and practice innovation.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.168
GPT teacher head0.365
Teacher spread0.197 · 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.

Study designNot applicable
Domainnot available
GenreReview

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 routes1
Has abstractyes

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