Validating the diagnostic code for acne in a tertiary care dermatology centre
Bibliographic record
Abstract
BACKGROUND: Administrative databases provide valuable patient data and are used to conduct population-based studies. However, no studies have been conducted to validate the codes for dermatological conditions. OBJECTIVE: To evaluate the validity of ICD 9 code 706 for acne. METHODS: This was a retrospective chart review of patients seen in dermatology clinics at Sunnybrook Health Sciences Centre between March 1 and May 31, 2013. The billing code for a clinic visit was compared to the diagnosis documented in the medical chart. RESULTS: There were 4,248 participants; 201 with an ICD-9 code of acne. This code had a PPV and sensitivity with 95% confidence intervals (CI) of 84.58% (78.67-89.13%) and 86.29% (80.51-90.62%), respectively. The specificity was 99.20% (98.86-99.45%). CONCLUSIONS: We showed that ICD-9 code 706 can be used to accurately identify patients with acne in a dermatology setting. This information can be applied to future epidemiologic studies.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".