Autonomic dysreflexia and telehealth.
Bibliographic record
Abstract
With the rapid expansion of telehealth lines in the United States and the establishment of provincially funded lines in Canada, a growing number of people use this convenient approach to establish their initial health needs and to pursue self-care. Telehealth providers, for the most part, rely on electronic protocols to offer triaging and advice. Unfortunately, standard protocols are misleading when a premorbid health condition such as a spinal cord injury is present. Symptoms such as a headache, diaphoresis or an elevated blood pressure, which are common occurrences in the general population, may indicate an emergency situation, namely autonomic dysreflexia (AD), when a mid-thoracic and higher spinal cord lesion is present. Since there is no available electronic protocol on AD, this emergency health condition is not recognized by the telehealth provider and may put the caller at risk of serious morbidity or even death. In this article, the authors present the clinical features of AD, the precipitating factors and the nursing management of an episode. The merits and pitfalls of electronic protocols are reviewed and an algorithm is presented to assist telehealth providers in recognizing AD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".