Review of thromboprophylaxis in otolaryngology-head and neck surgery.
Bibliographic record
Abstract
UNLABELLED: RESEARCH TYPE: Translational. OBJECTIVE: To review and tabulate the incidence of thromboembolic complications following head and neck surgery. STUDY DESIGN: Review. METHODS: Articles were identified using the MEDLINE database search engine. The relevant articles were reviewed and any thromboembolic complications were tabulated. RESULTS: Six articles, published between 1976 and 2007, were identified that reported on thromboembolic complications following head and neck surgery. Of these articles, four were retrospective reviews and two were prospective. Four of the studies looked at various methods of routine prophylaxis, which included several combinations of low-dose heparin, low-molecular-weight heparin, graduated compression stockings, and intermittent pneumatic compression devices. Two studies were simply investigating complications in general following head and neck surgery. CONCLUSIONS: Head and neck cancer patients are likely at higher risk than commonly thought, and venous thromboembolism is likely much more common that what is clinically evident. It is important to develop an institutional system of risk stratification to correspond to standardizations of thromboprophylaxis that are generally accepted. Although many institutions are already attempting to do so, such as we have outlined above by extrapolating from other surgical departments, it is important to show these relationships with head and neck patients specifically to justify the high cost of these various therapies.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".