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Record W4402581255 · doi:10.1097/spc.0000000000000727

Bridging the care gap: radiation therapy in elderly and frail cancer patients

2024· review· en· W4402581255 on OpenAlexaff
Caroline Hircock, Shing Fung Lee, Srinivas Raman, Elizabeth Chuk, Adrian Wai Chan, Edward Chow, Henry C. Y. Wong

Bibliographic record

VenueCurrent Opinion in Supportive and Palliative Care · 2024
Typereview
Languageen
FieldMedicine
TopicManagement of metastatic bone disease
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentrePrincess Margaret Cancer CentreUniversity Health NetworkUniversity of TorontoMcMaster University
Fundersnot available
KeywordsMedicineBridging (networking)Radiation therapyMEDLINEPalliative careCancerIntensive care medicineFamily medicineNursingInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: This review aims to address the gap in radiation therapy (RT) care for elderly cancer patients. It will discuss the barriers to implementing effective RT in elderly and frail patients with a focus on breast cancer and metastatic settings. RECENT FINDINGS: Recent studies indicate that SBRT provides better pain control for bone metastases compared to cEBRT, but elderly patients are underrepresented in these trials. Evidence on the effectiveness of geriatric assessment tools in predicting RT tolerance and toxicity is mixed, with some studies showing a correlation while others do not. Comprehensive geriatric assessments, though promising, are often impractical due to time and resource constraints. SUMMARY: There is a critical need for more inclusive research to better understand the risks and benefits of RT in elderly patients. Developing streamlined geriatric assessment tools and integrating them into clinical practice can enhance treatment personalization. Future studies should prioritize elderly populations to generate robust data, thereby improving RT outcomes and quality of life for this growing patient group.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.894
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.140
GPT teacher head0.448
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations4
Published2024
Admission routes1
Has abstractyes

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