F.06 Our institution’s experience with in-patient falls on the Neurosurgery ward
Bibliographic record
Abstract
Background: Neurosurgery patients are at higher risk of falls given the morbidity associated with their neurological disease. We present our department’s experience with in-patient falls. Methods: We analyzed our hospital’s database for Neurosurgery in-patient falls from January 1st till December 31st, 2015. Results: Of 1,317 patients admitted under Neurosurgery, 5% (n=63) had in-patient falls. CT head was done in 24% (n=15) of patients who had a fall and 93% (n=14) of the CT head post-fall was reported as no significant interval change. The combined cost of repeat CT imaging reporting no interval changes was approximately $ 7,000. One CT head post-fall showed worsening midline shift but did not impact management. One of the 78% (n=48) post-fall patients who did not get a CT head progressed to coma requiring emergent surgery and another patient suffered an isolated hip fracture requiring operation. 41% (n=26) of falls were from bed and 37% (n=22) were while ambulating. Leading diagnosis of in-patient falls was subdural hematoma (33%, n=21) and tumour (32%, n=20). Conclusions: Identification of risk factors for in-patient falls can reduce hospitalization costs. The highest number of in-patient falls occurs in patients with subdural hematoma and are likely to occur from a patient’s bed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".