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Record W2606256303 · doi:10.1111/iju.13351

Editorial Comment from Dr Porta <i>et al</i>. to Patterns of care among patients receiving sequential targeted therapies for advanced renal cell carcinoma: A retrospective chart review in the <scp>USA</scp>

2017· editorial· en· W2606256303 on OpenAlexaff
Camillo Porta, Laura Cosmai, Pietro Previtali

Bibliographic record

VenueInternational Journal of Urology · 2017
Typeeditorial
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsUniversity Hospital Foundation
Fundersnot available
KeywordsMedicineRenal cell carcinomaOncology

Abstract

fetched live from OpenAlex

In this issue of the journal, Pal et al. reported on patterns of care among metastatic renal cell carcinoma in the USA.1 At present, the metastatic renal cell carcinoma therapeutic landscape in industrialized countries resembles, to a certain extent, the one shown therein: a growing number of metastatic renal cell carcinoma patients are receiving several lines of treatments sequentially, and this abundance of options has undoubtedly improved their survival. However, this situation also encompasses some risks, irrespective of the fact that unexpected disparities between countries in the (once) rich and industrialized Western world are emerging. First, the availability of so many active agents could also contribute to non-virtuous behaviors. Indeed, if one can easily move from one agent to another in the event of toxicities, even only a few days after commencing the treatment, why try to adapt the treatment dosage and schedule, and/or aggressively apply supportive measures in order to keep the treatment going? An unusual scenario, one might say. Not really; this is quite often observable in everyday clinical practice, although almost no scientific papers have ever dealt with this issue. If so, it is clear that all the knowledge we have gathered over the years might simply vanish at the first difficulty, or in the presence of a complaint (although often justified) from the patient.2 On the whole, is this possibility an opportunity, or a pitfall? Both, but with a disturbing trend towards the latter case. Indeed, managing toxicities by just shifting from one agent to the other, just because the latter is perceived as (or even is) less toxic, not only deprives a given patient of an option (which, in many countries, cannot be resumed later), but also potentially has a detrimental impact on the treatment outcome.3 Despite a fast approval system empowered by the European Medicines Agency, a second emerging issue is the disparity among European countries in the real availability of novel anticancer agents. Several years ago, Tim Eisen strongly criticized the British system for denying the reimbursability of several kidney cancer drugs due to economic considerations.4 Although difficult to accept from a patient's perspective, such a decision was based on serious pharmacoeconomical and macroeconomical considerations. What in recent years has happened in Italy is definitely more difficult to understand; denying highly active treatments to cancer patients just because the Italian Agency for Drugs “fights” pharmaceutical companies over drug prices is really hard to accept. This is just an ultimately useless way to save money, without any serious attempt to prioritize expenses by evaluating how much expense is worthwhile for a given clinical benefit. That's why the magnitude of clinical benefit scale recently empowered by the European Society of Oncology should be applauded, offering governments sound instruments to decide if, how and where to allocate resources in a tough global economic situation.5 However, the subsequent necessary step would (and should) be the real application of such an instrument. The choice of not taking any responsibility, but rather to pass these responsibilities on to those who produce and sell the drugs, thus denying patients (i.e. those whom a government regulatory body should serve) therapeutic opportunities and probably months of life, is not the answer. Fortunately enough, a medical oncologist now chairs the Italian Agency for Drugs, hopefully bringing patients (and not drugs) back to center stage. CP acted as a consultant and/or speaker for Pfizer, Novartis, BMS, Ipsen, Eisai, EUSA, Peloton and Jannsen. LC and PP declare no conflict of interest.

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)0.001
metaresearch head score (Gemma)0.002
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.363
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0020.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.267
Teacher spread0.255 · 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
GenreEditorial

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
Published2017
Admission routes1
Has abstractyes

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