Does a Geriatric Oncology Consultation Modify the Cancer Treatment Plan for Elderly Patients?
Bibliographic record
Abstract
BACKGROUND: This study was performed to describe the treatment plan modifications after a geriatric oncology clinic. Assessment of health and functional status and cancer assessment was performed in older cancer patients referred to a cancer center. PATIENTS AND METHODS: Between June 2004 and May 2005, 105 patients 70 years old or older referred to a geriatric oncology consultation at the Institut Curie cancer center were included. Functional status, nutritional status, mood, mobility, comorbidity, medication, social support, and place of residence were assessed. Oncology data and treatment decisions were recorded before and after this consultation. Data were analyzed for a possible correlation between one domain of the assessment and modification of the treatment plan. RESULTS: Patient characteristics included a median age of 79 years and a predominance of women with breast cancer. About one half of patients had an independent functional status. Nearly 15% presented severe undernourishment. Depression was suspected in 53.1% of cases. One third of these patients had >2 chronic diseases, and 74% of patients took > or =3 medications. Of the 93 patients with an initial treatment decision, the treatment plan was modified for 38.7% of cases after this assessment. Only body mass index and the absence of depressive symptoms were associated with a modification of the treatment plan. CONCLUSION: The geriatric oncology consultation led to a modification of the cancer treatment plan in more than one third of cases. Further studies are needed to determine whether these modifications improve the outcome of these older patients.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".