Assessment of costs associated with adverse events in patients with cancer
Bibliographic record
Abstract
Adverse event (AE)-related costs represent an important component of economic models for cancer care. However, since previous studies mostly focused on specific AEs, treatments, or cancer types, limited information is currently available. Therefore, this study assessed the incremental healthcare costs associated with a large number of AEs among patients diagnosed with some of the most prevalent types of cancer. Data were obtained from a large US claims database. Adult patients were included if diagnosed with and treated for one of the following cancer types: breast, digestive organs and peritoneum, genitourinary organs (including bladder and ovary and other uterine adnexa), lung, lymphatic and hematopoietic tissue, and skin. Treatment episodes were defined as the period from initiation of the first antineoplastic pharmacologic therapy to discontinuation (i.e., gap of ≥ 45 days), or change in treatment regimen, or end of data availability. A total of 36 AEs were selected from the product inserts of 104 treatments recommended by practice guidelines. A retrospective matched cohort design was used, matching a treatment episode with a certain AE with a treatment episode without that AE. A total of 412,005 patients were selected, for a total of 794,243 treatment episodes, resulting in 1,617,368 matched treatment episodes across all 36 AEs. Incremental healthcare costs associated with AEs of any severity ranged from $546 for cough/upper respiratory infections to $24,633 for gastrointestinal perforation. The three most costly AEs when considering any severity were gastrointestinal perforation ($24,633), central nervous system hemorrhage ($24,322), and sepsis/septicemia ($23,510). Incremental healthcare costs associated with severe AEs ranged from $15,709 for dermatitis and rash to $48,538 for gastrointestinal fistula. The three most costly severe AEs were gastrointestinal fistula ($48,538), gastrointestinal perforation ($41,281), and central nervous system hemorrhage ($38,428). In conclusion, AEs during treatment episodes for cancer were frequent and associated with a substantial economic burden.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".