Macroglossia in neurosurgery
Bibliographic record
Abstract
Abstract Macroglossia, an abnormal swelling of the tongue, is a rare post-operative complication often associated with serious airway obstruction and prolonged intubations. Currently, there is a paucity of information on the true incidence, aetiology, and complications associated with macroglossia. A thorough review of the literature was carried out so as to summarise the characteristics of reported cases of macroglossia and to present potential treatments and preventive strategies. A literature search was conducted in PubMed to identify human case reports of macroglossia after neurosurgical procedures including spine, published in English from 1974 to December 2015. A total of 26 reports with 36 cases of macroglossia were identified. Macroglossia was most commonly reported after sub-occipital and/or posterior fossa craniotomies and spine surgeries in prone or park-bench positions. It is more common after procedures lasting >8 h. The aetiology of macroglossia is multi-factorial and possible mechanisms included local mechanical tongue compression interfering with venous and/or lymphatic drainage, regional venous thrombosis and/or local trauma. Complications included airway obstruction, re-intubation, difficult re-intubation, prolonged intubation and Intensive Care Unit stay and tongue necrosis. Prevention, awareness of the possibility, and early recognition are the best forms of treatment.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.003 | 0.001 |
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".