Expanding the Clinical Spectrum of Head Snap Sign
Bibliographic record
Abstract
Ethical Compliance Statement: Ethical approval was not required for this single-patient case report in accordance with the policies of our institutional ethics committee. 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. Verbal and written informed consent for publication and authorization to share videotape was obtained from the patient. Funding Sources and Conflict of Interest: 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: IE, BÇA, MYS, NHY, GK declare that there are no additional disclosures to report. AZ reports to have relevant proctorship, consultancy and honorarium relationships with Boston Scientific, NeuroBio, Abbott, and Insightec. AML reports receiving consultancies from Boston Scientific, Abbott, Functional Neuromodulation and Insightec. AF reports receiving consultancies from Abbvie, Ceregate, Medtronic, Boston Scientific, Iota, Inbrain, Inbrain Pharma; honoraria from Abbvie, Medtronic, Boston Scientific, Sunovion, Chiesi farmaceutici, UCB, Ipsen; grants from University of Toronto, Weston foundation, Abbvie, Medtronic, Boston Scientific, CIHR. No datasets were generated or analyzed during the preparation of this case report.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".