Effect of Obesity on Perioperative Outcomes Following Lung Cancer Surgery: Protocol for a Meta-Analysis and Systematic Review (Preprint)
Bibliographic record
Abstract
BACKGROUND Surgical resection is the primary curative treatment for early-stage lung cancer—the leading global cause of cancer mortality, responsible for nearly 1 in 5 cancer deaths in 2022. Obesity is a global health concern that may influence surgical outcomes; yet, its impact on perioperative outcomes following lung cancer surgery remains controversial. OBJECTIVE This protocol outlines a meta-analysis and systematic review to evaluate the association between obesity and perioperative outcomes in patients who underwent a lung cancer resection. METHODS Observational studies related to patients with lung cancer who underwent surgical resection were searched in 5 English and 3 Chinese literature databases: PubMed, Embase, Cochrane Library, Web of Science, MEDLINE, Chinese National Knowledge Infrastructure, Wanfang, and the Chinese Biomedical Database. The search period for these 8 electronic databases was from inception to 2025. The PROSPERO database and the International Platform of Registered Systematic Review and Meta-Analysis Protocols (INPLASY) database were also searched. Qualified studies were screened and selected by 2 authors independently. The literature obtained were imported into NoteExpress to screen the titles and abstracts. After reading the full text of the remaining studies, the final number of studies were determined. Two reviewers independently extracted data from the included studies by using a predesigned data extraction tool. The Newcastle-Ottawa Scale was used to evaluate the quality of the research. The primary outcome of this study was to evaluate the postoperative mortality in people living with obesity undergoing lung cancer surgical procedures. The secondary outcomes were the postoperative complications, average length of stay, blood loss during the operation, and operation time in people living with obesity undergoing lung cancer surgical procedures. For dichotomous data, we plan to present results as risk ratios with 95% CIs. For continuous data, we will use mean difference with 95% CIs. The Review Manager software (version 5.4) will be used for the meta-analysis and statistical analysis. Sensitivity analysis and Egger test will be performed with Stata software (version 16.0). RESULTS The results are not yet accessible because this is a protocol for a systematic review and meta-analysis. The protocol is registered in PROSPERO under the registration number CRD42025648330. By August 26, 2025, we completed the literature search of the 8 databases and completed the selection and extraction of data. CONCLUSIONS This study will synthesize existing evidence to clarify whether obesity is a risk factor for adverse outcomes or if it confers a protective effect, as suggested by the obesity paradox. These findings will guide clinical decision-making and improve perioperative care for obese people with lung cancer. CLINICALTRIAL PROSPERO CRD42025648330; https://www.crd.york.ac.uk/PROSPERO/view/CRD42025648330 INTERNATIONAL REGISTERED REPORT PRR1-10.2196/76315
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
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 | 0.056 | 0.112 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.026 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.076 | 0.006 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".