[Evidence summary:] What characterizes atypical clinical presentation of COVID-19 in older patients [age 65+]?
Bibliographic record
Abstract
Older people with COVID-19 often present with atypical clinical symptoms. The HSPC1 states: “It is important to remember that elderly people often present atypically with symptoms such as: lethargy; increased confusion; change in baseline condition; loss of appetite.” The WHO3and CDC4 designate anorexia, malaise, muscle pain, sore throat, dyspnea, nasal congestion, headache, confusion, rhinorrhea, hemoptysis, vomiting and diarrhea as atypical or less common symptoms. BMJ Best Practice5 and UptoDate6 both describe common and atypical clinical presentations of COVID-19; less common symptoms include headache, sore throat, chest pain, haemoptysis, dizziness, confusion, nasal congestion, gastrointestinal disorders, anorexia, smell and taste disorders, and skin disorders. Canadian guidance also lists atypical symptoms including delirium, functional decline, weakness, malaise, abdominal pain, unexplained tachycardia, anosmia, functional decline, conjunctivitis, falls, diarrhoea and decrease in blood pressure. The international literature describes a variety of atypical presentations from patients who presented with symptoms of acute ischaemic stroke; to altered mental status, dizziness and syncope; confusion and lethargy. Other atypical symptoms mentioned in the literature include chills, malaise, sore throat, confusion, myalgia, headache, nausea, rash, back pain, gastrointestinal disorders, tremor and tachycardia.
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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 | 0.003 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".