Comparing self‐reported and clinically diagnosed unmet dental treatment needs using a nationally representative survey
Bibliographic record
Abstract
OBJECTIVES: To describe the validity and diagnostic accuracy of self-reported data compared with clinically assessed data for the ascertainment of clinical dental treatment needs in the Canadian population. METHODS: A secondary analysis of data from the Canadian Health Measures Survey (2007-2009) was undertaken. Clinical treatment needs were classified into preventive and diagnostic, restorative, endodontic, periodontic, surgical, and orthodontic categories. Sensitivity, specificity, positive and negative predictive values (NPVs), kappa statistics and likelihood ratios (LR) were calculated to compare self-reported and clinically determined needs. Survey weights were applied to generate nationally representative findings of the Canadian population. RESULTS: Generally across most dental need categories, agreement between self-reported and clinically-determined dental need was found to be moderate to poor (kappa <0.6). For most needs, self-reported data was found to be highly specific (>90 percent) but not very sensitive. Low positive (<60 percent) and high NPVs (>80 percent) revealed that self-reported information was found to be more precise in reassuring when most dental needs were not present, opposed to confirming needs that were required. High positive LRs were obtained for endodontic (+LR = 12.15) and orthodontic needs (+LR = 14.82), indicating good diagnostic accuracy of positive self-report for these outcomes. CONCLUSIONS: Our findings suggest that in general, self-reports are poor estimates for normative dental treatment needs but do have some merit in confirming non-needs. Exceptionally, self-reports do have suitable diagnostic accuracy for predicting orthodontic and endodontic needs.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".