Pain and Neurocognitive Outcomes After Non-Cardiac Surgery in Older Adults
Bibliographic record
Abstract
Neurocognitive events after surgery, can occur as an acute event, such as postoperative delirium (POD) or in the form of decline in cognitive performance in the early or delayed postoperative period such as postoperative cognitive dysfunction (POCD). Neurocognitive events are patient- important outcomes which are associated with an increased risk of adverse outcomes. Surgery has been suggested to be a trigger for POD and to be associated with cognitive decline after surgery. Postoperative pain is common after surgery, and it is biologically plausible for pain to play a role in the development of neurocognitive outcomes. This thesis comprises six chapters focusing on pain and neurocognitive outcomes after non-cardiac surgery in older patients. Chapter 1 is an introduction and rationale for the included studies. Chapter 2 is a protocol for a series of systematic reviews to summarize the evidence regarding the association between postoperative pain (acute and chronic) and opioid-sparing pain management strategies for acute and chronic postoperative pain, and the incidence of POD and POCD. Chapter 3 reports the results of a systematic review and dose-response meta-analysis of observational studies evaluating the association of postoperative pain and POD and POCD. Chapter 4 describes a methodological approach to evaluate the robustness of meta-analyses with POD as an outcome to the variation in the methods (timing and frequency) of POD assessment. Chapter 5 presents the results of the Co-TELESURGE study, a longitudinal prospective cohort study of perioperative cognitive trajectories in older adults who were waiting for elective non-cardiac surgery during the COVID-19 pandemic. Chapter 6 discusses the key findings, limitations, implications for research, future recommendations and final conclusions of the research presented in this doctoral thesis.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".