Bibliographic record
Abstract
Case presentationA 44-year-old male presents to an urban emergency department (ED) after falling down 12 stairs.He was found by friends, in bed with a scalp laceration and another pool of blood at the foot of the stairs.Patient history included frequent alcohol ingestion.At the time of presentation, the patient smelled of liquor.The scalp laceration was sutured, and the patient was discharged home.Two days later, the patient presents to an urgent care facility with complaint of increased headache, event amnesia, balance disorder, photophobia and hyperacousis.No focal motor neurological deficits were noted on exam.Since the urgent care facility does not have a CT scanner, the patient was transferred to another urban ED for a CT scan of the head.The CT revealed a right parietal skull fracture and small subdural hematoma.After consultation with neurosurgery, the patient was admitted for observation for two days and again discharged home.He returned again to the urgent care facility five weeks later with post-concussive symptoms of continual headache and mood swings.He was again transferred to the urban ED for a follow-up CT scan.This case raises a few issues.The one I will focus on is the strong connection between alcohol use and head trauma.Patients continue to "fall through the cracks" when it is assumed that their main problem is alcohol intoxication and the possibility of more severe head injury is not adequately addressed.There are a few practices that I would like to discuss.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.027 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 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".