Is “Appearing Chronically Ill” a Sign of Poor Health? A Study of Diagnostic Accuracy
Bibliographic record
Abstract
OBJECTIVE: To determine the sensitivity and specificity of a physician's assessment that a patient "appears chronically ill" for the detection of poor health status. METHODS: The health status of 126 adult outpatients was determined using the 12-Item Short Form Health Survey (SF-12). Physician participants (n = 111 residents and faculty) viewed photographs of each patient participant and assessed whether or not the patient appeared chronically ill. For the entire group of physicians, the median sensitivity and specificity of "appearing chronically ill" for the detection of poor health status (defined as SF-12 physical health score below age group norms by at least 1 SD) were calculated. The study took place from February 2009 to January 2011. RESULTS: Forty-two participants (33%) had an SF-12 physical health score ≥1 SD below age group norms, and 22 (18%) had a score ≥2 SD below age group norms. When poor health status was defined as an SF-12 physical score ≥1 SD below age group norms, the median sensitivity was 38.1% (IQR 28.6-47.6%), specificity 78.6% (IQR 69.0-84.0%), positive likelihood ratio 1.64 (IQR 1.42-2.15), and negative likelihood ratio 0.82 (IQR 0.74-0.87). For an SF-12 physical score ≥2 SD below age group norms, the median sensitivity was 45.5% (IQR 36.4-54.5%), specificity 76.9% (IQR 66.3-83.7%), positive likelihood ratio 1.77 (IQR 1.49-2.25), and negative likelihood ratio 0.75 (IQR 0.66-0.86). CONCLUSIONS: Our study suggests that a physician's assessment that a patient "appears chronically ill" has poor sensitivity and modest specificity for the detection of poor health status in adult outpatients. The associated likelihood ratios indicate that this assessment may have limited diagnostic value.
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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 | 0.018 | 0.162 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".