New Parameter of Hemoglobin Status as an Indicator of Efficacy of Preoperative Angioembolization in Extracranial Hypervascular Tumours
Bibliographic record
Abstract
BACKGROUND: Routinely, the standard for measuring the success of preoperative embolization procedure as an adjunct in the management of head and neck vascular tumours has been to evaluate the amount of blood loss, duration of surgery, and intraoperative neurovascular injuries. OBJECTIVE: We hypothesized that the rate of change in the preoperative hemoglobin status would more accurately and objectively reflect the effectiveness of the embolization technique. MATERIALS AND METHODS: Twenty-six patients with extracranial vascular tumours were divided into two groups (A and B) of 13. Group A patients had preoperative embolization and group B patients directly underwent surgery. The difference between the preoperative and postoperative hemoglobin levels and the percentage rate of change of hemoglobin status were calculated. RESULTS: The percentage rate of change of preoperative to postoperative hemoglobin is less in group A (9.43%) when compared with group B (18.27%). The ratio of preoperative to postoperative hemoglobin in the two groups is also statistically significant (1:1.9). CONCLUSIONS: The percentage rate of change of preoperative to postoperative hemoglobin and the ratio of preoperative to postoperative hemoglobin are more accurate and objective parameters for assessment of success of preoperative embolizations rather than other variables such as intraoperative blood loss or duration of surgery.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".