Smoking habits and gallbladder disease: a systematic review and meta-analysis study.
Bibliographic record
Abstract
BACKGROUND: It has been claimed that smoking is linked with an increased risk for gallbladder disease (GBD); however, related issues need further consolidation and clarification. The present systematic review and meta-analysis aimed to further investigate the potent correlation between GBD and smoking. METHODS: We conducted a comprehensive literature review to identify every study published from January 1989 to December 2019, reporting risk estimates regarding GBD and smoking. The random-effect, generic inverse variance method, according to description by DerSimonian and Laird, was used to compute pooled estimates. We used the Newcastle-Ottawa quality assessment scale to appraise the included studies' quality. RESULTS: =96 %, 95 % CI: 62-100 %, p <0.001). Publication bias was non-significant (Eggers' regression p =0.072). The main sources of heterogeneity, as detected by meta-regression analyzing study characteristics, biases and confounders, were non-adjustment for family history (p =0.007) and alcohol (p =0.020), respectively. Subgroup analysis indicated a comparable risk for GBD as far as current, former and ever smokers are concerned (p =0.520). Quantitative analysis suggested a dose-effect for current smoking and GBD (p =0.010). CONCLUSIONS: Non-smokers were demonstrated to be at a lower risk of presenting GBD when compared with ever smokers; all relevant risk estimates necessitate adjustment for family history and alcohol intake. HIPPOKRATIA 2020, 24(4): 147-156.
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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.037 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".