Adrenal surgery in England: better outcomes in high‐volume practices
Bibliographic record
Abstract
AIMS AND BACKGROUND: Adrenal surgery is performed by a variety of surgical specialities in differing environments and volumes. International data suggest that there is a correlation between adrenal surgery volume and outcomes but there are no UK data to support this or UK surgical guidelines. A multidisciplinary team representing the stakeholders in adrenal disease is preparing a national guidance on adrenal surgery. A review of the outcomes for adrenal surgery in England was performed to correlate outcomes with the volume of surgeon practice. METHODS: Hospital Episode Statistics (HES) data for the National Health Service (NHS) in England in the tax year 2013-2014 were examined for adrenal surgery. Length of hospital stay and rate of postoperative readmission were assessed as surrogate quality markers and a comparison made between 'high-' and 'low-' volume surgeons. RESULTS: A total of 795 adult adrenalectomies were performed by 222 different surgeons with a range of between 1 and 34 adrenalectomies performed per surgeon. Only thirty-six (16%) adrenal surgeons performed 6 or more adrenalectomies. A total of 186 surgeons (84%) performed a median of one adrenalectomy a year. Length of stay and readmission rate within thirty days of operation was 60% longer and 47% higher, respectively, when performed by low-volume surgeons. CONCLUSION: The current provision of adrenal surgery in the UK is not in the best interests of patients and is not cost-effective for the NHS. Adrenal surgery is best performed by higher volume surgeons in centres with dedicated adrenal multidisciplinary teams expert in all aspects of care of the adrenal patient.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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".