Bibliographic record
Abstract
Reduced kidney function around the time of surgery is an important risk factor for postoperative mortality. Despite this there is limited information on how reduced kidney function prior to surgery alters prognosis, what causes sudden decrements in kidney function after surgery (known as acute kidney injury), or how they might be avoided. The studies in this thesis inform these knowledge gaps. Chapter 2 describes the results of a post hoc analysis of the interaction between preoperative estimated glomerular filtration rate, a marker of kidney function, and postoperative cardiac troponin T, a marker of heart damage, for predicting 30-day mortality in a prospective cohort study of patients undergoing noncardiac surgery. Chapter 3 uses administrative and clinical data from a single centre to inform the risk of acute kidney injury after noncardiac surgery by concentrations of preoperative hemoglobin and change in postoperative hemoglobin. Chapter 4 uses the same data to determine a definition of intraoperative hypotension that is prognostic of acute kidney injury, myocardial injury and death. Chapter 5 describes a randomized controlled trial that compares a novel therapeutic procedure called remote ischemic preconditioning to a sham procedure in patients undergoing cardiac 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 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".