Incidence and complications of hypothyroidism postlaryngectomy: A systematic review and <scp>meta‐analysis</scp>
Bibliographic record
Abstract
BACKGROUND: Hypothyroidism is common postlaryngectomy and is associated with laryngectomy-specific complications. The objective of this study is to determine the incidence and predictors of hypothyroidism postlaryngectomy and its associated complications. METHODS: Systematic review, data extraction, and meta-analyses were performed following the PRISMA protocol. Six databases were searched for studies reporting on postlaryngectomy thyroid status with incidence, risk factors, management, or complications. RESULTS: Fifty-one studies with 6333 patients were included. The pooled incidence of postlaryngectomy hypothyroidism is 49% (CI 42%-57%). Subgroup analysis showed postlaryngectomy hypothyroidism rates significantly correlated with hemithyroidectomy and radiotherapy. Patients who underwent laryngectomy, hemithyroidectomy, and radiotherapy had a 65% (CI 59%-71%) rate of hypothyroidism; laryngectomy and hemithyroidectomy 46% (CI 33%-60%); laryngectomy and radiotherapy 26% (CI 19%-35%); and laryngectomy alone 11% (CI 4%-27%) (p < 0.001). CONCLUSIONS: Laryngectomized patients with partial thyroidectomy or radiation therapy are at significant risk of postoperative hypothyroidism. Evidence-based protocols for early detection and (prophylactic) treatment should be established.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.020 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".