Utility of the Edmonton Frail Scale (EFS) in identifying frail elderly patients during treatment of colorectal cancer (CRC).
Bibliographic record
Abstract
e14001 Background: CRC is the second leading cause of cancer-related death, and 40-50% of patients are older than 70 years. Frailty is a concept that has been proposed by geriatricians as an indicator of functional age. The EFS is a 15 point incremental scale; it is quick (<5 min), and simple to administer. We conducted a pilot study to establish if the EFS would add utility beyond clinician’s expertise. The primary objective was to determine if there was an association between the EFS and receipt of chemotherapy. Methods: The EFS was administered to stage II-IV CRC patients ≥70 years, referred to a Medical Oncologist at a tertiary care centre. The EFS was completed by one of the investigators, with the treating oncology team blinded to results and a follow up 14 month chart review. Results: Forty-six patients were enrolled with the following characteristics: average age 76, 48% male, 78% performance status (PS) 0-1, and 21 (46%) started chemotherapy. The EFS was reproducible between visits (r = 0.81 [CI 0.64-0.9, p<0.0001]). There was no correlation between the EFS and receipt of chemotherapy for the study population as a whole. As none of the 16 stage II patients had high-risk features requiring chemotherapy, the analysis was repeated excluding these patients. There was a reduced likelihood of receiving chemotherapy for stage III/IV patients with higher EFS scores (Odds ratio 0.56 [CI 0.37-0.85, p<0.01] per unit increment). A similar effect was observed after multivariable analysis (adjusting for PS, age, stage and gender, Odds ratio 0.41 [CI 0.18-0.96, p<0.05] per unit increment). No correlation existed between EFS and upfront dose reductions, choice of less toxic regimens, or hospitalization secondary to grade 3/4 toxicities. Conclusions: This pilot study suggests the EFS can identify patients that Oncologists may have thought were too frail for chemotherapy, independent of PS. Therefore, the EFS has the potential to add a reproducible, and quantifiable measure of frailty to the clinician’s decision making armamentarium. The next study phase will employ the EFS real-time, and determine if using the EFS can minimize complications and unplanned health care utilization in elderly cancer 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 | 0.004 | 0.010 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".