Autonomic dysreflexia: recognizing a common serious condition in patients with spinal cord injury.
Bibliographic record
Abstract
OBJECTIVE: To raise family physicians' awareness of autonomic dysreflexia (AD) in patients with spinal cord injury (SCI) and to provide some suggestions for intervention. SOURCES OF INFORMATION: MEDLINE was searched from 1970 to July 2011 using the terms autonomic dysreflexia and spinal cord injury with family medicine or primary care. Other relevant guidelines and resources were reviewed and used. MAIN MESSAGE: Family physicians often lack confidence in treating patients with SCI, see them as complex and time-consuming, and feel undertrained to meet their needs. Family physicians provide a vital component of the health care of such patients, and understanding of the unique medical conditions related to SCI is important. Autonomic dysreflexia is an important, common, and potentially serious condition with which many family physicians are unfamiliar. This article will review the signs and symptoms of AD and offer some acute management options and preventive strategies for family physicians. CONCLUSION: Family physicians should be aware of which patients with SCI are susceptible to AD and monitor those affected by it. Outlined is an approach to acute management. Family physicians play a pivotal role in prevention of AD through education (of the patient and other health care providers) and incorporation of strategies such as appropriate bladder, bowel, and skin care practices and warnings and management plans in the medical chart.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".