The Dutch diagnostic model for laboratory animal allergen sensitization was generalizable in Canadian apprentices.
Bibliographic record
Abstract
OBJECTIVE: To assess the transportability of an existing diagnostic questionnaire model for the sensitization to laboratory animal (LA) allergens. STUDY DESIGN AND SETTING: The model was externally validated in 414 Canadian animal health apprentices. Several approaches were used: (1) no adjustment; (2) recalibration of the intercept of the model; (3) re-estimation of the intercept and the regression coefficients of predictors; and (4) model revision, by excluding the existing predictor(s) and/or including new predictor(s). The bootstrapping procedure was done following the third and fourth methods. The calibration was assessed graphically and with the Hosmer-Lemeshow (HL) test. Discriminative properties were determined by the area under the receiver operating characteristic curve (ROC area). RESULTS: When applied without adjustment, the model's discriminative ability was adequate (ROC area was 0.74 vs. the original ROC area of 0.76); the calibration was poor (HL test P0.10) and reasonable discrimination (ROC area ranged between 0.73 and 0.75). The refitted and revised model showed a good internal validity (correction factor from the bootstrapping procedure was more than 0.90). CONCLUSION: Once updated, the diagnostic model is valid and can be applied with reasonable performance in an animal health apprentice setting.
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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 | 0.010 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".