An Online Health Community for Aneurysmal Subarachnoid Hemorrhage Patients: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: Aneurysmal subarachnoid hemorrhage (aSAH) is a condition affecting relatively young patients and has high rates of morbidity and mortality. Online health communities have emerged to fill the void for patient advocacy and information, allowing individuals with shared experiences and chronic disorders to connect. OBJECTIVE: We have developed an online health community for aSAH patients, and this pilot study was conducted to evaluate it from a patient's perspective. METHODS: We implemented an online, members-only, health community (MijnSAB, translation: MySAH) in addition to the usual aSAH care at Radboudumc, Nijmegen, the Netherlands. A questionnaire that was sent to consecutive aSAH patients was used to evaluate the usability and utility of MySAH. Answers were provided using a 5-point Likert scale. There was also one open-ended question asking about what was missing from the MySAH tool. RESULTS: In total, 66 consecutive patients with aneurysmal subarachnoid hemorrhage were informed about the online health community. Of 64 potential MySAH users, 26 patients gained access to MySAH, 20 of whom were willing to participate in the evaluation. Those who used the community were younger (P=.03) and in a better condition at discharge (P=.03). The patients were positive about MySAH's contribution to the quality of their care, but not to their quality of life. Most patients (18/20, 90%) reported that they would recommend the community to others in their position. Open suggestions on how to improve the tool included more frequent blogs, including by a rehabilitation specialist. CONCLUSIONS: This pilot study showed that the online health community, MySAH, has a beneficial effect on the aftercare of patients suffering from aSAH because it gives easy access to relevant information provided by peers or caregivers. Due to the variable clinical outcomes after aSAH, the tool will mainly be useful for a select group of patients (with a better clinical outcome).
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".