Bayesian estimation of the prevalence of osteoarthritis in the Québec elderly population from an administrative database
Bibliographic record
Abstract
0-494-06414-5 L'auteur a accord une licence non exclusive permettant la Bibliothque et Archives Canada de reproduire, publier, archiver, sauvegarder, conserver, transmettre au public par tlcommunication ou par l'Internet, prter, distribuer et vendre des thses partout dans le monde, des fins commerciales ou autres, sur support microforme, papier, lectronique et/ou autres formats.L'auteur conserve la proprit du droit d'auteur et des droits moraux qui protge cette thse.Ni la thse ni des extraits substantiels de celle-ci ne doivent tre imprims ou autrement reproduits sans son autorisation.Conformment la loi canadienne sur la protection de la vie prive, quelques formulaires secondaires ont t enlevs de cette thse.Bien que ces formulaires aient inclus dans la pagination, il n'y aura aucun contenu manquant.From the first day when 1 met my directors, to the final day of submission, 1 knew that 1 was well surrounded. 1 want to deeply thank my directors for their incredible professionalism throughout my studies and research at the department of Epidemiology and Biostatistics of McGill University.Elham Rahme, and Lawrence Joseph, thank you for everything, for moral support, academic support, and your immense intellectual support.From the numerous corrections, and reviews of my work, 1 have learned a lot.Thank you for your patience and the time you took to read again and again those chapters, and for all your precious advice. 1 am grateful towards you for transmitting me your passion for science. 1 admire the enthusiasm that you demonstrate for your profession, and the way
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".