Long‐Term Survival and Risk of Institutionalization in Onco‐Geriatric Surgical Patients: Long‐Term Results of the PREOP Study
Bibliographic record
Abstract
OBJECTIVES: To evaluate long-term survival and institutionalization in onco-geriatric surgical patients, and to analyze the association between these outcomes and a preoperative risk score. DESIGN: Prospective cohort study with long-term follow-up. SETTING: International and multicenter locations. PARTICIPANTS: Patients aged 70 years or older undergoing elective surgery for a malignant solid tumor at five centers (n = 229). MEASUREMENTS: We assessed long-term survival and institutionalization using the Preoperative Risk Estimation for Onco-geriatric Patients (PREOP) score, developed to predict the 30-day risk of major complications. The PREOP score collected data about sex, type of surgery, and the American Society for Anesthesiologists classification, as well as the Timed Up & Go test and the Nutritional Risk Screening results. An overall score higher than 8 was considered abnormal. RESULTS: We included 149 women and 80 men (median age = 76 y; interquartile range = 8). Survival at 1, 2, and 5 years postoperatively was 84%, 77%, and 56%, respectively. Moreover, survival at 1 year was worse for patients with a PREOP risk score higher than 8 (70%) compared with 8 or lower (91%). Of those alive at 1 year, 43 (26%) were institutionalized, and by 2 years, almost half of the entire cohort (46%) were institutionalized or had died. A PREOP risk score higher than 8 was associated with increased mortality (hazard ratio = 2.6; 95% confidence interval [CI] = 1.7-4.0), irrespective of stage and age, but not with being institutionalized (odds ratios = 1 y, 1.6 [95% CI = .7-3.8]; 2 y, 2.2 [95% CI = .9-5.5]). CONCLUSION: A high PREOP score is associated with mortality but not with remaining independent. Despite acceptable survival rates, physical function may deteriorate after surgery. It is imperative to discuss treatment goals and expectations preoperatively to determine if they are feasible. Using the PREOP risk score can provide an objective measure on which to base decisions. J Am Geriatr Soc 68:1235-1241, 2020.
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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 | 0.002 |
| 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 | 0.001 |
| 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".