Validation of a case definition for speech and language disorders: In community-dwelling older adults in Alberta.
Bibliographic record
Abstract
OBJECTIVE: To validate a case definition for speech and language disorders in community-dwelling older adults and to determine the prevalence of speech and language disorders in a primary care population. DESIGN: This is a combined case definition validation and cross-sectional prevalence study. Chart review was considered the reference standard and was used to estimate prevalence. This study used de-identified electronic medical record data from participating SAPCReN-CPCSSN (Southern Alberta Primary Care Research Network-Canadian Primary Care Sentinel Surveillance Network) primary care clinics. SETTING: Southern Alberta. PARTICIPANTS: Men and women aged 55 years and older who had visited a SAPCReN-CPCSSN physician or nurse practitioner at least once in the 2 years before the beginning of the study. MAIN OUTCOME MEASURES: Validation analysis included estimation of sensitivity, specificity, positive predictive value, and negative predictive value. Prevalence was the other main outcome measure. RESULTS: The prevalence of speech and language disorders within the sample of 1384 patients was 1.2%. The case definition had a favourable specificity (99.9%, 95% CI 99.6% to 100.0%), positive predictive value (75.6%, 95% CI 25.4% to 96.6%), and negative predictive value (99.0%, 95% CI 98.8% to 99.2%). Sensitivity was not sufficient for validity (18.8%, 95% CI 4.05% to 45.6%). CONCLUSION: The case definition did not meet an acceptable standard for validity and thus cannot be used for future epidemiologic research. However, owing to the case definition's high positive predictive value, it might be useful for clinical purposes and for cohort studies. Finally, while the case definition did not prove valid, this study has provided a conservative estimate of prevalence (1.2%) given the case definition's high specificity.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".