Global incidence and case fatality rate of pulmonary embolism following major surgery: a protocol for a systematic review and meta-analysis of cohort studies
Bibliographic record
Abstract
BACKGROUND: Pulmonary embolism (PE) is a life-threatening condition common after major surgery. Although the high incidence (0.3-30%) and mortality rate (16.9-31%) of PE in patients undergoing major surgical procedures is apparent from findings of contemporary observational studies, there is a lack of a summary and meta-analysis data on the epidemiology of postoperative PE in this same regard. Hence, we propose to conduct the first systematic review to summarise existing data on the global incidence, determinants and case fatality rate of PE following major surgery. METHODS: Electronic databases including MEDLINE, EMBASE, SCOPUS, WHO global health library (including LILACS), Web of Science and Google scholar from inception to April 30, 2017, will be searched for cohort studies reporting on the incidence, determinants and case fatality rate of PE occurring after major surgery. Data from grey literature will also be assessed. Two investigators will independently perform study selection and data extraction. Included studies will be evaluated for risk of bias. Appropriate meta-analytic methods will be used to pool incidence and case fatality rate estimates from studies with identical features, globally and by subgroups of major surgical procedures. Random-effects and risk ratio with 95% confidence interval will be used to summarise determinants and predictors of mortality of PE in patients undergoing major surgery. DISCUSSION: This systematic review and meta-analysis will provide the most up-to-date epidemiology of PE in patients undergoing major surgery to inform health authorities and identify further research topics based on the remaining knowledge gaps. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42017065126.
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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 | 0.072 | 0.111 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.019 | 0.040 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".