SEVEN YEARS FOLLOW-UP POST SUBARACHNOID HEMORRHAGE
Bibliographic record
Abstract
Background: Impaired cognition, fatigue, depression and anxiety are consequences described after a subarachnoid hemorrhage (SAH), but long-term follow-ups are lacking. Aim: To investigate the long-term consequences among survivors seven years post SAH.Methods: In this explorative study, home-visits was carried out in a cohort of survivors with non-traumatic SAH in Gothenburg, Sweden. The forms and questionnaires used were Barthel Index (BI), modified Rankin Scale (mRS), Hospital Anxiety and Depression Scale (HADS), Montreal Cognitive Assessment (MoCA) and Multidimensional Fatigue Inventory (MFI).Results: Out of the 33 patients that fulfilled the inclusion criteria, 18 (55%) participated with a mean age of 65.5. Cognitive impairment was present in 11 participants, assessed with the MoCA, where the item of delayed recall was most difficult. The participants had high independency in ADL (BI). However, the number of participants free from disability according to the mRS was low (n=3). Nearly half of the participants had symptoms of anxiety (n=8) and experienced fatigue assessed with the MFI. Conclusion: Seven years post SAH, the majority of participants reported disability, such as cognitive impairment and anxiety. The hidden long-term consequences of SAH should be considered when planning the healthcare including follow-up as well as the rehabilitation. Keywords: Subarachnoid hemorrhage, follow-up studies, cognition
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".