The developing world of pre‐operative optimisation: a systematic review of Cochrane reviews
Bibliographic record
Abstract
Pre-operative optimisation is a heterogenous group of interventions aimed at improving peri-operative outcomes. To understand the evidence for pre-operative optimisation in the developing world, we systematically reviewed Cochrane reviews on the topic according to the Human Developmental Index (HDI) of the country where patient recruitment occurred. We used summary statistics and cartograms to describe the HDI, year of publication, timing of pre-operative intervention and risk of bias associated with each included trial. We assessed the impact of multinational trials on the risk of bias introduced by countries of differing HDI. Four-hundred and nine trials representing 51 countries and 89,389 randomly allocated participants were summarised in this review. Four-hundred and nineteen out of 451 (93%) trial populations (i.e. a group of study participants from one country) were from high and very high HDI countries. The median (IQR [range]) HDI of countries were 0.862 (0.806-0.892 [0.445-0.949]). Three of the 409 included trials were multinational, representing 32 countries and 37,736 out of 89,389 (42.2%) included participants. Africa was the least represented continent, with only 4 included trials and 566 participants, of which 62.3% were from one multinational trial. The overall risk of bias was high or unclear in 381 out of 409 (93%) trials. Inclusion of multinational trials decreased the proportion of trial populations introducing high or unclear risk of bias by 9.4% (95%CI 5.1-13.7; p < 0.0001). Half of the world's population live in low- and middle-HDI countries. This population is poorly represented in systematically reviewed evidence on pre-operative optimisation. Multinational trials increase the knowledge contribution from low- and middle-HDI countries and decrease risk of bias in systematic reviews.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".