The significance of CT diagnosis for the selection of type of operation in orbital space occupying lesions
Bibliographic record
Abstract
Objective:To investigate the CT features of intraorbital space occupying lesions,so as to conduct the treatment and approaches to orbitotomy.Methods:We analysed the CT features,clinical presentations,methods of treatment and approaches to orbitotomy in 59 cases of orbital space occupying lesions,most of which had been confirmed by operation and pathology.Results:Thirty five cases of benign tumor,6 cases of primary malignant tumor,11 cases of idiopathic pseudotumor and 7 cases of carotid cavernous fistula were found in this group.All cases of CCF were diagnosed accurately by CT scanning and treated with embolization.Forty one orbital tumors and 8 IOPT were completely resected in virtue of CT scanning.Conclusion:CT can locate the lesions and characterize the nature of the lesions,and play important role in the selection of the treatment and approaches to orbitotomy.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".