Redefining postoperative monitoring in chronic subdural hematoma: the promise of trans-burr hole sonography
Bibliographic record
Abstract
Dear Editor, Chronic subdural hematoma (CSDH) is a prevalent neurosurgical condition, particularly among the elderly, and is often treated effectively with burr hole surgery1–3. Currently, Computed tomography (CT) scanning is the gold standard for postoperative evaluation of CSDH1,2. It offers detailed information about the brain, allowing for the assessment of residual hematoma, brain re-expansion, and potential complications. However, the drawbacks are significant, including high radiation doses and the logistical burdens of scheduling and performing CT scans, which can be particularly challenging in less mobile patients1,2. A recent study explored an innovative approach—trans-burr hole sonography—as a potential alternative to reduce radiation exposure and enhance patient safety without compromising diagnostic accuracy4. The study conducted on 20 patients postburr hole surgery utilized ultrasound through the surgical burr hole to assess the status of the subdural space, specifically measuring residual fluid thickness. Remarkably, the study demonstrated that sonographic measurements closely matched those obtained by CT, with discrepancies averaging only 1.2 mm for axial views and 1.4 mm for coronal views4. The strong positive correlations reported between CT and ultrasound measurements (r values exceeding 0.9) are encouraging4. They not only validate the accuracy of sonography in this new application but also underscore its potential as a primary monitoring tool5. The use of trans-burr hole ultrasound for postoperative evaluation of CSDH has huge implications for neurosurgical practice. The most significant advantage of sonography is the absence of ionizing radiation. This is crucial considering the cumulative radiation doses patients may receive from multiple follow-ups, particularly those with recurrent conditions or complications. Also, ultrasound equipment is portable and can be used at the bedside, making it ideal for use in ICU or for patients with mobility issues. This accessibility significantly reduces the logistical challenges associated with transporting patients to the radiology suite. Moreover, ultrasound is generally less expensive than CT, both in terms of the equipment required and the cost per examination. This cost-effectiveness can make a substantial difference in healthcare settings where resources are limited6. Despite the promising results, the adoption of trans-burr hole sonography faces challenges. The primary obstacle noted was the presence of air in the burr hole or subdural space, which can interfere with ultrasound waves and affect image quality4. However, this issue tends to resolve over time as postoperative changes stabilize. Future refinements in ultrasound technology and technique could mitigate this limitation, perhaps through the development of software algorithms designed to compensate for the presence of air or through modified procedural protocols that reduce air introduction during surgery. Moreover, the study’s findings are limited due to small sample size and study design. Further research is necessary to confirm these findings in a larger and more diverse patient cohort. Prospective multicenter studies would be invaluable, providing broader validation and facilitating the identification of patient-specific factors that may influence the efficacy of the technique. In conclusion, trans-burr hole sonography stands on the cusp of becoming a transformative approach in the management of CSDH postoperatively. It offers a safer, less invasive, and more patient-friendly alternative to CT scans. As such, trans-burr hole sonography could soon redefine postoperative monitoring, making it a standard practice in the management of CSDH and potentially other conditions requiring neurological monitoring. Ethical approval Ethical approval is not applicable for this correspondence article. Consent Informed consent is not applicable for this correspondence article. Sources of funding Not applicable. Author contribution A.A.: conceptualization, project administration, supervision, validation, writing – original draft, and writing – review and editing; P.S., R.K.S., D.S., M.A., M.N.K., S.G., Q.S.Z., and S.R.: supervision, validation, writing – review and editing. Conflicts of interest disclosure No conflict of interest to declare. Research registration unique identifying number (UIN) Not applicable. Guarantor Ayush Anand. Data availability statement Not applicable. Provenance and peer review Not commissioned, externally peer-reviewed. Assistance with the study Not applicable.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".