Comparing the DePaul Symptom Questionnaire with physician assessments: a preliminary study
Bibliographic record
Abstract
Background: Diagnostic assessment of chronic fatigue syndrome (CFS) and myalgic encephalomyelitis (ME) is largely based on a two part process; screening patients who might meet criteria and following up this assessment with physicians’ clinical evaluation of a range of inclusionary symptoms and exclusionary illnesses. Purpose: The aim was to assess how well the DePaul Symptom Questionnaire (DSQ) screened for patients who were ultimately diagnosed by physicians using the Canadian Consensus Criteria (CCC). Methods: Sixty-four patients referred for evaluation of possible CFS or ME were screened initially using the DSQ, and then evaluated and subsequently diagnosed by physicians. To assess the consistency between the self-report DSQ and the physicians’ diagnosis, sensitivity and specificity as well as predictive values were calculated. Results: The DSQ identified 60 and the physicians identified 56 as having a CCC diagnosis. The overall agreement between the two ratings on the diagnostic assessment part was moderate (Kappa = 0.45, p < .001). The sensitivity of DSQ was good (98%) while the specificity was 38%. Positive and negative predictive values were 92% and 75%, respectively. Conclusion: DSQ is useful for detecting and screening symptoms consistent with a CCC diagnosis in clinical practice and research. However, it is important for initial screening of self-report symptoms to be followed up by subsequent medical and psychiatric examination in order to identify possible exclusionary medical and psychiatric disorders.
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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 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".