Identifying biomarkers of ventilator induced lung injury during one-lung ventilation surgery: a scoping review
Bibliographic record
Abstract
Background: Ventilator-induced lung injury (VILI) can occur as a result of mechanical ventilation to two lungs. Thoracic surgery often requires one-lung ventilation (OLV). The potential for VILI is likely higher in OLV. The impact of OLV on development of post-operative pulmonary complications is not well understood. We aimed to perform a scoping review to determine reliable biomarkers of VILI after OLV. Methods: A scoping review was performed using Cochrane Collaboration methodology. We searched Medline, EMBASE and SCOPUS. Gray literature was searched. Studies of adult human or animal models without pre-existing lung damage exposed to OLV, with biomarker responses analyzed were included. Results: After screening 5,613 eligible papers, 89 papers were chosen for full text review, with 29 meeting inclusion. Approximately half (52%, n=15) of studies were conducted in humans in an intra-operative setting. Bronchoalveolar lavage (BAL) & serum analyses with enzyme-linked immunosorbent assay (ELISA)-based assays were most commonly used. The majority of analytes were investigated by a single study. Of the analytes that were investigated by two or more studies (n=31), only 16 were concordant in their findings. Across all sample types and studies 84% (n=66) of the 79 inflammatory markers and 75% (n=6) of the 8 anti-inflammatory markers tested were found to increase. Half (48%) of all studies showed an increase in TNF-α or IL-6. Conclusions: A scoping review of the state of the evidence demonstrated that candidate biomarkers with the most evidence and greatest reliability are general markers of inflammation, such as IL-6 and TNF-α assessed using ELISA assays. Studies were limited in the number of biomarkers measured concurrently, sample size, and studies using human participants. In conclusion these identified markers can potentially serve as outcome measures for studies on OLV.
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 | 0.014 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".