Developing a comprehensive cancer specific geriatric assessment tool
Bibliographic record
Abstract
BACKGROUND: Population aging is one of the most distinctive demographic events of this century. United Nations projections suggest that the number of older persons is expected to increase by more than double from 841 million in 2013 to >2 billion by 2050. It is estimated that 60% of the elderly patients may be affected by cancer and may present in the advanced stage. The aim of this paper was to develop a brief cancer-specific comprehensive geriatric assessment tool for use in a geriatric population with advanced cancer that would identify the various medical, psychosocial, and functional issues in the older person. METHODS: Literature on assessment of geriatric needs in an oncology setting was reviewed such that validated tools on specific domains were identified and utilized. The domains addressed were socioeconomic, physical symptoms, comorbidity, functional status, psychological status, social support, cognition, nutritional status and spiritual issues. Validated tools identified were Kuppuswamy scale (socioeconomic), Edmonton Symptom Assessment Scale (Physical symptoms) and SAKK cancer-specific geriatric assessment tool, which included six standard geriatric measures covering five geriatric domains (comorbidity, functional status, psychological status, social support, cognition, nutritional status). The individual measures were brief, reliable, and valid and could be administered by the interviewer. CONCLUSION: The tool was developed for use under the geriatric palliative care project of the department of palliative medicine at Tata Memorial Hospital, Mumbai. We plan to test the feasibility of the tool in our palliative care set-up, conduct a needs assessment study and based on the needs assessment outcome institute a comprehensive geriatric palliative care project and reassess outcomes.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".