Rationalising preoperative group and save in thyroidectomy: a thyroid volume-based predictive mode
Bibliographic record
Abstract
Abstract Background Routine pre-operative Group and Save (G&S) for thyroidectomy often leads to unnecessary laboratory workload and inefficient resource use. This project evaluates G&S necessity based on preoperative ultrasound, histopathology thyroid volume and intraoperative estimated blood loss (EBL) to establish a data-driven protocol. Method This quality improvement project was conducted from a retrospective chart review of all thyroidectomies performed from 2023 to date. Data included preoperative and histopathological thyroid volume, intraoperative EBL, and pre-operative G&S and cross-matching orders. Significant bleeding was defined as EBL ≥ 150 ml, as defined by current available literature. Receiver Operating Characteristic (ROC) curve analysis identified an optimal ultrasound volume threshold and histopathology volume threshold predicting EBL ≥ 150 ml. Results A total of 119 procedures were included (46 total and 73 hemithyroidectomies). Mean estimated intraoperative EBL was 108ml (median = 50 ml; range = 10–1500 ml) in total thyroidectomies and 72 ml (median = 30ml; range = 5–1000 ml) in hemithyroidectomies. Only 1 patient (0.74%) required transfusion. Preoperative ultrasound thyroid volume and histopathological thyroid volume demonstrated a strong correlation with EBL and exhibited excellent predictability for EBL ≥ 150 ml (AUC = 0.936, 95% c.i. = 0.836–1.035, P < 0.001), with an optimal cut-off at 0.61 dm3 (sensitivity = 87.5%, specificity = 97.1%). Two pre-operative G&S samples were ordered in all cases with a total estimated cost of £4760. Our optimal cut-off would have saved £4280 by requiring G&S for only 12 patients. Conclusion Transfusion during thyroid surgery is exceedingly rare, while thyroid volume strongly predicts high intraoperative blood loss. Implementing a data-driven threshold (for example volume > 0.61 dm3) may allow for more selective pre-operative G&S testing, optimizing resource use and reducing unnecessary blood orders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".