Using the Risk Assessment Index to predict mortality and quality of life in breast and gynecologic cancer patients.
Bibliographic record
Abstract
239 Background: Cancer patients vary considerably in health status making it challenging to evaluate the risk of complications from cancer treatment. To aid oncologists in identifying patients with highest risk for adverse outcomes, we investigated the Risk Assessment Index (RAI), a validated tool used to assess frailty in patients prior to elective surgery. We assessed whether the RAI could serve to predict mortality, hospital utilization, and quality of life in cancer patients. Methods: Participants were breast and gynecological cancer patients treated at UPMC Magee Women’s Cancer Center who completed the RAI between July 2016 and December 2017. Patients completed patient reported outcomes (PROs) during each visit including the Short Form (SF)-12, Edmonton Symptom Assessment, anxiety and depression screens, and MD Anderson Symptom Inventory (MDASI) and were analyzed up to 180 days from the RAI date. Mortality was assessed at 90, 180, and 365-day intervals, and hospital utilization was assessed within 90-days of RAI. Results: There were 1,764 unique breast and gynecological cancer patients. Significant correlations between the RAI and mortality were observed for both groups with frail patients having higher rates of mortality at each interval. Frailty was associated with higher rates of hospitalization compared to non-frail patients (31% vs 20%, p = 0.05 & 50% vs 34%, p = 0.02 for breast and gynecologic patients, respectively). Frailty correlated with fair/poor ratings on the SF-12 for breast and gynecologic patients (r = 0.13, p = 0.01; r = 0.37 p < 0.001, respectively). On the Edmonton, frailty correlated with lower ratings of well-being in breast cancer patients (r = 0.11, p = 0.012) and higher symptom burden in gynecological patients (r = 0.23, p = 0.01). No correlations were observed between the RAI and anxiety or depression. For gynecologic patients, there were significant correlations between the RAI and MDASI with frail patients having higher rates of pain, fatigue, appetite, diarrhea, and memory. Conclusions: We demonstrated that the RAI is correlated with mortality, self-reported quality of life, and hospitalizations in breast and gynecologic cancer patients. Using this tool to risk-stratify patients may help to guide shared decision-making discussions and provide appropriate treatment and/or supportive services for this vulnerable population.
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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 | 0.006 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".