Minimally invasive approach to the child with palpitations
Bibliographic record
Abstract
Palpitations are a common reason for referral to a pediatric cardiologist. Although generally benign, palpitations are a significant cause for concern in the individual and their family. Similarly, palpitations may be the initial presentation of significant heart disease, resulting in heightened concern in the referring physician. Although emphasis is usually placed on excluding arrhythmia as the cause for palpitations, there are a variety of noncardiac causes for palpitations. The patient history and physical examination are the key components of the evaluation and guide subsequent investigations. In many cases, an immediate diagnosis cannot be made and additional testing may be required; this often includes further monitoring for episodes, cardiac imaging and ambulatory monitoring. Current technologies for ambulatory monitoring during symptoms include Holter monitoring and a variety of patient-activated event recorders, including implantable loop recorders. Each presents its own unique advantages and disadvantages to aid diagnosis in the management of a child with palpitations. The primary focus for the clinician is to determine whether the etiology is benign in nature or whether there is underlying heart disease that may carry a more serious prognosis.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".