The impact of cancer therapy on cognition in the elderly
Bibliographic record
Abstract
Cancer and cancer therapy-related cognitive impairment (formerly known as chemobrain or chemo-fog) are often described in the literature. In the past, studies have failed to prove the existence of cancer therapy-related cognitive dysfunction. However, more recently, prospective trials have shown that patients undergoing chemotherapy do display impairment in specific cognitive domains. Aging confers an increased risk of developing cancer, as well as cognitive impairment. The Geriatric Oncology clinic of the Segal Cancer Centre, Jewish General Hospital in Montreal was founded in 2006 to address the unique needs of older cancer patients. We will describe two cases of cancer therapy-related cognitive impairment from our Geriatric Oncology clinic. The first case is that of a 75 year old male diagnosed with stage III non-small cell lung carcinoma who complained of forgetfulness since starting carboplatin-paclitaxel. The second case is that of a 65 year old female diagnosed with stage I, estrogen-receptor-positive breast cancer who had undergone lumpectomy followed by adjuvant cyclophosphamide, methotrexate and fluorouracil chemotherapy, radiation therapy and was on exemestane when she was evaluated. We will also briefly review the literature of cancer therapy-related cognitive impairment.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".