Preoperative Assessment of Geriatric Surgical Patients: Update on Clinical Scales Used for Elective General and Digestive Surgery
Bibliographic record
Abstract
BACKGROUND: Higher life expectancy in the general population entails a growing interest in the surgical management of diseases affecting elderly patients. Preoperative assessment when planning surgery needs to carefully evaluate physical and functional status of the patient. This review aims to describe the most commonly used scales in the evaluation of elderly patients scheduled for surgery and provides a useful tool to decide the scales that would be better to assess these specific patients. METHODS: According to the PRISMA statement of publications published, we have carried out a systematic review focused on elderly patients who underwent surgical procedures in General and Surgery. Using Medline, Embase, and Cochrane library, a systematic search of the literature from 1992 to 2018 was performed. This enabled us to retrieve information from the selected articles on scales to evaluate medical fitness, functional status, or both, in the elderly or frail patients. RESULTS: We reviewed 102 articles and selected the most frequently used assessment scales or indexes. After this extensive analysis, we selected 4 functional scales (Katz Index, Barthel Scale, Karnofsky Performance Score, and Vulnerable Elders Survey), 4 clinical scales (American Society of Anaesthesiologists Index, Charlson Comorbidity Index, Pfeiffer Test, and Physiological and Operative Severity Score for the enumeration of Mortality and Morbidity Scale) and finally, 2 mixed scales (American College of Surgeons National Surgical Quality Improvement Program Surgical Risk Calculator and Edmonton Frail Scale). CONCLUSIONS: No consensus on the use of a unified assessment scale for elderly patients exists. However, with this review, we provide a brief guideline about the most useful and used scales to perform a comprehensive assessment of geriatric patients undergoing surgery.
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 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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".