The Lymph Node Yield of Neck Dissections – Is There A Difference Between Consultant Surgeons and Specialist Registrars?
Bibliographic record
Abstract
Objective: To assess adherence to previously published guidelines in acoustic neuroma screening.Acoustic neuromas commonly present with asymmetrical sensorineural hearing loss.Strict criteria for asymmetry have been developed to appropriately and cost-effectively scan (MRI) for this tumour.Setting: Otolaryngology department of a teaching hospital with a tertiary referral lateral skull base practice.Method: Review of 100 patients in whom MRI scans had been requested for asymmetrical sensorineural hearing loss was undertaken.Their audiograms were compared with guidelines giving specific audiometric criteria for scanning previously published by our department 6 years previously.Results: whilst the protocol was adhered to in many cases, there was a significant number in which scans were inappropriately requested, as the asymmetry did not meet the expected audiometric criteria.This may be due to frequent turnover of junior staff, which has increased in recent years.Conclusion: A re-education programme was undertaken and new staff will be made aware of the guidelines at their departmental induction.Appropriate requests for MRI scans will have cost savings and prevent unnecessary patient anxiety.
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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".