P.123 Is there an association between geographical location of patients in NS and management of unruptured, incidental intracranial aneurysm?
Bibliographic record
Abstract
Background: Managing unruptured cerebral aneurysms involves monitoring or repair, with complex factors influencing decision-making. Geographical distance from treatment centers is an understudied factor. This study explores a potential relationship in Nova Scotia between proximity to the sole neurosurgical center in Halifax and aneurysm management. Methods: A prospectively collected neurosurgery database was used to identify all adults seen for unruptured cerebral aneurysm between Jan 1, 2015 - Dec 31, 2020. Demographic data, aneurysm characteristics, follow-up and treatment information were collected. Univariate and multivariate analyses assessed management differences based on geography, controlling for relevant factors including aneurysm size and location. Results: Among 390 patients, 40% were in Halifax, and 60% were outside. No significant difference existed in elective repair (34% vs. 26%, p=0.143) and imaging follow-up frequency (2.26 vs. 2.22, p=0.858). In-person follow-up was higher within Halifax (1.83 vs. 1.43, p=0.008), while virtual follow-up was significant outside Halifax (1.44 vs. 1.01, p=0.003). Overall, in-person and elective repair frequencies declined with the COVID-19 peak, whereas virtual follow-up increased. Conclusions: No significant association was found between patient location and repair decisions. Patients in closer proximity had more in-person follow-ups, while those farther away had more virtual follow-ups. The COVID-19 pandemic affected follow-up frequencies universally.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.030 | 0.003 |
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".