Author response: Post-thyroidectomy bleeding: analysis of risk factors from a national registry
Bibliographic record
Abstract
We thank Idrees et al. for their interest in our paper1. Their reflection on the potential for devastation on the event of a bleed is well made and how the surgical community must strive to reduce this. In reply to their questions and based on the original dataset of 67 896 thyroidectomies before any exclusions. There is a high proportion of hemithyroidectomies as the registry reports on cases from surgeons of an endocrine and ENT background, so lobectomy will suffice for many benign and malignant nodules. Furthermore, 3797 of 67896 (5.6 per cent) have undergone staged thyroidectomy, i.e., an initial lobectomy and followed by a completion contralateral lobectomy and these may be counted as two cases. The issue of proportionally more hemithyroidectomies in the UK has previously been highlighted by others. We perform less thyroid surgery per annum in the UK (12 000) than other European countries (40–45000), despite having larger populations. In comparison, we perform less surgery for benign goitre and more surgery for clinically significant cancers. With the ATA guideline change we thus have many hemithyroidectomies for small/low risk cancers and as diagnostic procedures. We did look at vessel sealing technology, but this was not associated with bleeding in univariate analysis and so not included in multivariable analysis. Vessel sealing devices in addition to standard diathermy did not affect bleeding risk, so we didn't investigate individual devices, though we have that data. We were concerned about the potential for reporting bias and in future hope to be able to validate UKRETs by comparison with independent hospital episode statistics (HES) data. There are however two possible explanations. (1) The registry only collects bleed data on patients who require a return to theatre so some of the 169 surgeons reporting no bleeds may have had bleeds that were conservatively managed. (2) The time periods for which individual surgeons are reporting may also be much lower and hence they may not have had a bleed during this period. As 138 surgeons recorded fewer than 100 thyroidectomies in total and thus might perhaps not be expected to have had a bleed. Disclosure: The authors declare no conflicts of interest.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".