The Diagnostic Accuracy of Subjective Dyspnea in Detecting Hypoxemia Among Outpatients with COVID-19
Bibliographic record
Abstract
Abstract Objectives The majority of patients with mild-to-moderate COVID-19 can be managed using virtual care. Dyspnea is challenging to assess remotely, and the accuracy of subjective dyspnea measures in capturing hypoxemia have not been formally evaluated for COVID-19. We explored the accuracy of subjective dyspnea in diagnosing hypoxemia in COVID-19 patients. Methods This is a retrospective cohort study of consecutive outpatients with COVID-19 who met criteria for home oxygen saturation monitoring at a university-affiliated acute care hospital in Toronto, Canada from April 3, 2020 to June 8, 2020. Hypoxemia was defined by oxygen saturation <95%. Dyspnea measures were treated as diagnostic tests, and we determined their sensitivity (SN), specificity (SP), negative/positive predictive value (NPV/PPV), and positive/negative likelihood ratios (+LR/-LR) for detecting hypoxemia. Results During the study period 64/298 (21.5%) of patients met criteria for home oxygen saturation monitoring, and of these 14/64 (21.9%) were diagnosed with hypoxemia. The presence/absence of dyspnea had limited accuracy for diagnosing hypoxemia, with SN 57% (95% CI 30-81%), SP 78% (63%-88%), NPV 86% (72%-94%), PPV 42% (21%-66%), +LR 2.55 (1.3-5.1), -LR 0.55 (0.3-1.0). An mMRC dyspnea score >1 (SP 97%, 95%CI 82%-100%), Roth Maximal Count <12 (SP 100%, 95%CI 75-100%), and Roth Counting time < 8 seconds (SP 93%, 95%CI 66%-100%) had high SP that could be used to rule in hypoxemia, but displayed low SN (≤50%). Conclusions Subjective dyspnea measures have inadequate accuracy for ruling out hypoxemia in high-risk patients with COVID-19. Safe home management of patients with COVID-19 should incorporate home oxygenation saturation monitoring.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".