The Lymph Node Yield of Neck Dissections – Is There a Difference Between Consultant Surgeons and Specialist Registrars?
Bibliographic record
Abstract
and variable success rates reported.This study aimed to evaluate the role of PLAD application for recurrent hip instability following THA and its long-term outcomes.Patients and Methods: Patients undergoing PLAD were identified using hospital records coding data.Radiological and clinical data were analysed using the patient's hospital case-notes and electronic PACS system.Results: Data was available for 15 PLAD applications with an average age of 75.1 years.The mean follow-up period was 21.9 months.PLAD prevented further dislocation in 73% of patients.Long-term follow-up of patients with PLAD remaining in-situ demonstrated that 100% of patients were independently mobile at 2-4 years and all patients were pain-free after 1-year.Sub-group analysis of risk factors identified a significantly higher ASA grade to be associated with further episodes of dislocation in patients undergoing PLAD application.Discussion: Our results demonstrate that the majority of patients undergoing PLAD application return to independent mobility with no long-term hip pain.PLAD application should be used with caution in patients with an ASA grade of 3 or greater.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".