Head and neck cancer and non‐steroidal anti‐inflammatory drugs: Systematic review and meta‐analysis
Bibliographic record
Abstract
The objective was to assess the effects of non-steroidal anti-inflammatory drugs (NSAIDs) on head and neck cancer (HNC) outcomes. A systematic review was conducted following the PRISMA guidelines. The MEDLINE and the Cochrane Central Register databases were searched. Risk of bias was assessed by the Cochrane Collaboration's tool and by the Newcastle-Ottawa Scale. Meta-analyses were performed with the RevMan software. Seventeen articles met the inclusion criteria. Quality scores for observational studies ranged between 5 and 8 stars and the RCT was assessed as high risk of bias. NSAIDs use was associated with a 13% risk reduction of HNC (OR: 0.87 95% CI 0.77-0.99). NSAIDs use was associated with a 30% reduced cancer-specific mortality and with a 40% decreased risk on disease-recurrence. NSAIDs may have a modest protective effect on HNC risk and a positive impact on cancer-specific survival and disease-recurrence. The findings do not support a protective role of aspirin on HNC outcomes.
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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.025 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".