How Do I Do a Skin Punch Biopsy for Research and Diagnostic Purposes in Parkinson's Disease and Atypical Parkinsonism?
Bibliographic record
Abstract
Ethical Compliance Statement: The authors confirm that the approval of an institutional review board was obtained. Informed written consent was separately obtained for all videotapes taken as part of the routine clinical evaluation as per standard policy in the Toronto Western Hospital Movement Disorders Clinic. We confirm that we have read the journal's position on issues involved in ethical publication and affirm that this work is consistent with those guidelines. Funding Sources and Conflicts of Interest: The authors report that no specific funding was received for this work. The authors declare that there are no conflicts of interest relevant to this work. Financial Disclosures for the previous 12 months: A.E.L. has served as an advisor for AbbVie, Amylyx, Aprinoia, Biogen, BioAdvance, Biohaven, BioVie, BlueRock, BMS, Denali, EG427, Janssen, Lilly, Pharma 2B, Sun Pharma, and UCB; received honoraria from Sun Pharma, AbbVie, and Sunovion; received grants from Canadian Institutes of Health Research, Edmond J Safra Philanthropic Foundation, Krembil Brain Institute, the Michael J. Fox Foundation, Parkinson Foundation, Parkinson Canada, and The Rossy Foundation; is serving as an expert witness in litigation related to paraquat and Parkinson's disease, received publishing royalties from Elsevier, Saunders, Wiley-Blackwell, Johns Hopkins Press, and Cambridge University Press. D.A.O. reports support from the Michael J. Fox Foundation. Data sharing not applicable to this article as no datasets were generated or analysed during the current study. Data S1. Supporting information. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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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 | 0.027 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".