Tonsillectomy and Adenoidectomy Are Not Associated With an Altered Risk of Childhood-Onset Type 1 Diabetes
Bibliographic record
Abstract
Type 1 diabetes results from the autoimmune destruction of the pancreatic β-cells. As various studies have shown that tonsillectomy and adenoidectomy, particularly in childhood, impact the function of the immune system (1), it is possible that these procedures could increase a child’s risk of type 1 diabetes. Conversely, the hygiene hypothesis (2) suggests that frequent exposure to infections in early life may protect against type 1 diabetes. Therefore, children undergoing tonsillectomy and adenoidectomy may be expected to have a reduced risk of type 1 diabetes because they are likely to have experienced higher rates of respiratory and ear infections in early childhood than other children. Previously, a Finnish case-control study (3) of type 1 diabetes with adenoidectomy reported a modest though not significant increase in the risk of diabetes, while a possible association with tonsillectomy has been investigated only in a small Canadian case-control study (4). The aim of this study was to investigate, for the first time in a cohort setting, the risk of type 1 diabetes after tonsillectomy and/or adenoidectomy in children identified from hospital records. Northern Ireland hospitals routinely record the name, date of birth, and hospital number of each individual undergoing a surgical procedure. The type of procedure is …
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".