Transplante renal e risco de câncer de cabeça e pescoço : revisão sistemática e meta-análise
Bibliographic record
Abstract
Kidney transplantation and head and neck cancer risk: systematic review and meta-analysis Paulo Ricardo Saquete Martins Filho, Aracaju/SE, Brazil, 2013. Background: Kidney transplantation is considered the treatment of choice for end-stage kidney disease, but a wide-ranging excess risk of post-transplant malignancies has been recognized as a complication of long-term immunosuppression. De novo malignancies are important cause of morbidity and mortality in kidney recipients. We performed a systematic review and meta-analysis to determine the risk of head and neck cancer after kidney transplantation. Methods: A systematic search was performed in PUBMED, EMBASE, SCOPUS, and LILACS databases to identify cohort studies reporting on the risk of head and neck cancer in kidney recipients. The assessment of validity of selected studies was performed using the STROBE statement and the Newcastle-Ottawa Scale (NOS) for Cohort Studies. Only studies with NOS ≥ 6 were included in the meta-analysis. Pooled relative risks (RR) were calculated using the Mantel-Haenszel or DerSimonian-Laird method, depending on statistical heterogeneity. To detect publication bias, Egger‟s test, Duval and Tweedie‟s analysis, and leave-one-out sensitivity analysis were conducted. Results: A total of 9 high-quality cohort studies were included in the meta-analysis. The pooled RR of head and neck cancer after kidney transplantation was 8.2 (95% CI 4.0-16.6, p<0.0001). A significant excess risk of cancer was observed in the lip (RR = 43.6, 95% CI 24.2-78.4, p<0.0001). The pooled RR of oral cavity/pharynx and salivary gland was 3.5 (95% CI 2.5-5.0, p<0.0001) and 5.6 (95% CI 1.3-24.0, p = 0.020), respectively. No evidence of publication bias was observed. Conclusion: There is an increased risk of head and neck cancer after kidney transplantation. The head and neck should be examined routinely during the post-transplant surveillance.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.050 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".