Do elderly patients have the most to gain from laparoscopic surgery?
Bibliographic record
Abstract
Populations are aging worldwide, people are living longer, and the surgical needs of elderly patients are rising. Laparoscopic techniques have become more common with improved training, surgeon skill and evidence of improved outcomes. Benefits of laparoscopy include decreased blood loss, postoperative pain, and hospital length of stay; improved mobilization, quicker return to normal activity; and fewer pulmonary, thrombotic, and abdominal wall complications. Indeed, for many common pathologies laparoscopy has become the gold standard, unless contraindicated. It has been questioned as to whether elderly patients can reap the same benefits from laparoscopic surgery. The concern in elderly patients is that physiologic demands may outweigh the benefit seen in younger patients. This question stems from concerns related to longer operative times, increased technical challenge, as well as the impact of physiologic demands of pneumoperitoneum and patient positioning. However, with anesthesia and adequate perioperative cardiac care, there is no evidence that these factors lead to worse clinical outcomes in elderly patients. In contrast, perhaps elderly patients - with increased prevalence of multi-morbidity, geriatric syndromes and diminished physiologic reserve - have the most to gain from a laparoscopic approach.
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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".