Bibliographic record
Abstract
Chemotherapy Costs TO THE EDITOR: Schrage and Hanger provided a very helpful gleam on how medical oncologists viewed their responsibilities in communicating with patients about chemotherapy costs. There are two key issues that can be further elaborated. First, although many may think that they do not usually take cost into account when recommending a treatment, the truth is that we all do, if only subconsciously. Recommendation of a treatment, even when cost is not explicitly discussed, is based on the assumption that there are resources (somehow) to support the delivery of the treatment. To take an extreme theoretical example, would one recommend a treatment that can only be administered in zero gravity, or that would cost $1 billion per treatment course? Probably not, and the patient likely would concur that this is not a realistic option. The second consideration is that a treatment is not usually categorized as simply “works” or “doesn’t work.” More commonly, it works “a little” or “quite well.” Clinical trial design, and regulatory approval, is usually based on proof of the minimal level of clinical improvement. However, this may not be enough when balanced against other factors like treatment costs, burden of disease, toxicity profiles, and so on. Funding agencies, such as the Centers for Medicare & Medicaid Services, define different levels of clinical improvement in global terms, such as “more effective” (improves by a significant, albeit small, margin as compared with established services or medical items) and “as effective but with advantages” (same effect as established services or medical items, but some advantages that some patients will prefer). However, this provides no indication of the true magnitude of benefit relative to the baseline prognosis of the patients. For example, 9 months may mean something different for patients with advanced small-cell lung cancer to those with, say, prostate cancer. Sunstrum et al showed that improvement in survival, tumor response, quality of life, and toxicity would classify a drug as being of “substantial improvement” for the purpose of setting a price by the Canadian Patented Medicine Prices Review Board. What is needed now is to relate the size of improvement in these end points to the baseline prognosis of the patients. Only then can we truly discuss how to communicate chemotherapy costs with the patients—and to the society at large.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.017 | 0.036 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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 source (direct Gemma or distilled Codex), 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".