Postural orthostatic tachycardia syndrome (POTS): Priorities for POTS care and research from a 2019 National Institutes of Health Expert Consensus Meeting – Part 2
Bibliographic record
Abstract
The National Institutes of Health hosted a workshop in 2019 to build consensus around the current state of understanding of the pathophysiology of postural orthostatic tachycardia syndrome (POTS) and to identify knowledge gaps that must be addressed to enhance clinical care of POTS patients through research. This second (of two) articles summarizes current knowledge gaps, and outlines the clinical and research priorities for POTS. POTS is a complex, multi-system, chronic disorder of the autonomic nervous system characterized by orthostatic intolerance and orthostatic tachycardia without hypotension. Patients often experience a host of other related disabling symptoms. The functional and economic impacts of this disorder are significant. The pathophysiology remains incompletely understood. Beyond the significant gaps in understanding the disorder itself, there is a paucity of evidence to guide treatment which can contribute to suboptimal care for this patient population. The vast majority of physicians have minimal to no familiarity or training in the assessment and management of POTS. Funding for POTS research remains very low relative to the size of the patient population and impact of the syndrome. In addition to efforts to improve awareness and physician education, an investment in research infrastructure including the development of standardized disease-specific evaluation tools and outcome measures is needed to facilitate effective collaborative research. A national POTS research consortium could facilitate well-controlled multidisciplinary clinical research studies and therapeutic trials. These priorities will require a substantial increase in the number of research investigators and the amount of research funding in this area.
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 | 0.095 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.015 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".