Management of Cerebrospinal Fluid Leak followingPosterior Cranial Fossa Surgery
Bibliographic record
Abstract
OBJECTIVE: Cerebrospinal fluid leakage remains a significant cause of morbidity following posterior fossa surgery, and its treatment remains a difficult problem. The aim of the study was to propose a treatment algorithm for its management. METHODS: . Patients with post operative CSF leakage had either been treated initially with conservative measures including re-suturing of the wound, with CSF lumbar drainage to be employed in case the CSF leakage didn't stop, or the initial intervention was the institution of CSF lumbar drainage simultaneously with conservative measures. VP (ventriculo-peritoneal) shunt was done in patients with gross hydrocephalus on postoperative CT brain. RESULTS: There were 25 (17%) cases of CSF leakage, including 24 incisional CSF leaks and one case of CSF otorrhea. In eight patients with incisional CSF leakage treated initially with conservative measures including re-suturing of the wound, CSF leakage stopped in only two cases. CSF lumbar drainage instituted later on in six cases with persistent leakage stopped the CSF leakage. In fourteen patients managed initially with re-suturing of the wound and concomitant CSF lumbar drainage, CSF leakage settled in all the cases. Two patients with gross hydrocephalus on post operative CT were managed successfully with VP shunt. Re-suturing of the wound with concomitant CSF lumbar drainage was found to be significantly associated (p=0.003) with the stoppage of CSF leakage, and the settlement of meningitis (p= 0.014). CONCLUSION: lumbar drainage, instead of an initial trial of conservative therapy alone.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".