Bibliographic record
Abstract
IntroductionLung cancer is one of the leading causes of cancer in the population.The aging population is a reality.More and more, in oncology, we are facing challenges about management of cancer in this important population.In this chapter, we will review data on lung cancer and aging.We will explore how to evaluate this particular population to offer them the best treatment possible, bearing in mind that some adjustment may be necessary compared to younger population. Definition of the elderly Chronologic agingThe cut-off point for an adult to be considered "elder" is not well defined.According to most of the literature, age 70 years is used as a chronologic marker for definition of elderly population.This is also the age most commonly used in the clinical trials in oncology as a limit for recruitment (Balducci, 2000). Physiologic agingIn fact, elderly should be referring to a state either than a chronological age.It is well known that with time, especially at or around 70 years of age, a number of age-related physiologic changes occur affecting the physiologic reserve of the person.Many of these changes may affect the tolerance to cancer treatment as; decreased renal and liver functions, decreased volume of distribution, immune response and intestinal absorption (Avery et al., 2009).Talking more specifically about lung function, age-related pulmonary changes include a decreased response to hypoxemia or hypercapnia, decreased elasticity of the lung tissue, increased ventilation-perfusion mismatch, and decreased forced expiratory volume (Gonzalez-Aragoneses et al., 2009). The aging populationPopulation age is increasing in developed country.In the United States, in 2000, there were 35 million persons aged over 65 years old.This proportion is expected to continue to increase from 40 million in 2010 to 81 million in 2040 (U.S. Census Bureau).In Canada, the situation is similar.In 2036, Statistics Canada estimates the population of persons aged 70 years and older to be situated between 10 and 10.8 million.www.intechopen.com
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".