Referrals for Movement Disorder Surgery: Under-Representation of Females and Reasons for Refusal
Bibliographic record
Abstract
OBJECTIVE: Referral of movement disorder patients for deep brain stimulation surgery was examined to determine whether referred patients were representative of gender proportions in our population, and reasons why patients do not proceed to surgery. METHODS: Demographic information on referrals to the surgical program was retrospectively reviewed from our database and from a detailed chart review. RESULTS: Although almost equal numbers of movement disorder patients are male and female, of the 91 patients referred for surgery, only 31% were female. Sixty-one percent of referred patients did not undergo surgery. Of these, the majority were denied for medical reasons, including cognitive decline (21%), psychiatric concerns (5%) and neurological reasons (42%). CONCLUSIONS: Almost one-third of patients referred for movement disorder surgery were denied for medical reasons. This underscores the importance of evaluation of all potential patients by a multidisiplinary team to fully assess suitablity for stereotactic surgery. Interestingly, women were under-represented in those referred. In order that all appropriate patients have the opportunity to consider surgery, education of both physicians and patients, and different strategies to approach females regarding surgery may allow more patients to benefit from this treatment.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".