Experience of headache treatment in acute non-traumatic subarachnoid hemorrhage and evaluation of treatment effectiveness
Bibliographic record
Abstract
Aim. Assessment of approaches to the identification and treatment of headache in acute non-traumatic subarachnoid hemorrhage for further study of this problem and optimization of analgesic therapy.Methods. Medical records of 62 patients admitted to the hospital with acute non-traumatic subarachnoid hemorrhage (SAH) were retrospectively reviewed. The patients were divided into 2 groups: 19 patients underwent X-ray guided endovascular occlusion under local anesthesia and 43 patients had their aneurysm clipped under general anesthesia. Quantitative assessment of headache with the use of Visual Analog Scale (VAS) and qualitative assessment using McGill Pain Questionnaire were performed in all patients. Non-steroidal anti-inflammatory drugs (dexketoprofen, paracetamol) and opioid analgesics were used for analgesia.Results. It was found that the wide-spread approach to the relief of headaches in SAH is not effective enough that is especially evident from the results of the second group (average VAS score in group 1 was 4.7±2.9 versus 6.3±1.8 in group 2). Achieving adequate analgesia with opioids was accompanied with excessive sedation (Richmond Agitation-Sedation Scale score was -1-2 points) complicating timely evaluation of neurological status. McGill Pain Questionnare data received and insufficient effect of the used treatment regimen allow us to suggest presence of neuropathic component of headache in non-traumatic subarachnoid hemorrhage.Conclusion. Conventional methods and regimens of headache treatment are not effective enough so it is necessary to analyze protocol variants for headache evaluation and adequate treatment regimens considering pathogenetic mechanisms of pain in non-traumatic subarachnoid hemorrhage and the possibility of excessive sedation with opioid analgesics.
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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) | 0.001 | 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".