Geriatric preoperative assessment of elderly patients with gastrointestinal cancer: Clinical factors and prognostic implication.
Bibliographic record
Abstract
e20522 Background: Patient selection for surgical oncologic treatment is a challenge, particularly with elderly patients. The purpose of this study was to compare patient's characteristics with geriatrician recommendation as fit or non-fit for surgery and to identify baseline characteristics associated with adverse immediate postoperative outcomes. Methods: We conducted a retrospective study of patients seen in our geriatric oncology clinic before an elective surgical intervention for gastrointestinal cancer between 2010 and 2014. Patients were referred by surgeons or oncologists. Clinical and geriatric assessment variables and postoperative data were collected by chart review. Univariate analyses were used to identify baseline patient's characteristics associated with decision prior to surgery and with postoperative outcomes (length of hospital stay and discharge status). Results: Forty-four patients were included (14 had hepatic metastasis of a colorectal cancer, 13 had rectal cancer, 7 had pancreatic adenocarcinoma, 3 had colon cancer and 7 had other types). Median age was 80.1 years (70-89) and 70% were men. Eleven patients had chemotherapy or radiotherapy before the geriatric assessment. Nine patients (20.5%) were advised against surgery. These patients were more dependent for IADLs (p = 0.003), had lower grip strength ( < 20kg for woman and < 30kg for man) (p = 0.003) and lower gait speed (p = 0.029). Other characteristics were similar between the groups. Twenty-three patients were operated. The median hospital stay was 10 days. Eighteen patients (78%) had complications, 13 minors (Clavien 1-2) and 5 majors (Clavien > 2), including 1 death. Seven patients had delirium. Falls in the last 6 months (p = 0.022) and polypharmacy (p = 0.043) were associated with prolonged hospital stay. Eight patients (36%) were discharged in rehabilitation or convalescent unit, they had lower grip strength (p = 0.019). Conclusions: Low gait speed and grip strength seems to influence preoperative decisions in our geriatric oncology clinic. Falls in the last 6 months, polypharmacy and low grip strength are associated with adverse postoperative outcomes in our study.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".