145A Review of Cerebrospinal Fluid Samples Sent for Xanthochromia - A Necessary Test in the Diagnosis of Subarachnoid Haemorrhage?
Bibliographic record
Abstract
In diagnosing subarachnoid haemorrhage (SAH), a non-contrast CT brain within 6 hours of symptom onset can yield a diagnostic sensitivity of up to 100% [1]. Cerebrospinal fluid samples can be analysed for xanthochromia if the diagnosis remains in doubt. We studied whether the test for xanthochromia was useful and cost effective in diagnosing SAH in our hospital, given that it is processed in another institution with significant associated costs. A retrospective analysis was done of all xanthochromia tests done from January 2014 to October 2016. Further data was collected from medical records of patients with ambiguous test results. 206 requested tests for xanthochromia over 34 months with 178 results received. 167 (93.8%) results were negative, 10 (5.6%) ambiguous (e.g.“SAH not excluded”) and 1 (0.6%) was positive, leading to a diagnosis of SAH. None of those with ambiguous results were ultimately diagnosed with SAH. Xanthochromia is still a relevant test to diagnose SAH, but in a very small number of cases in our hospital. Given the proportion of positive tests (0.6%), we need to be more stringent about selecting patients who require this investigation. Xanthochromia is processed off site and costs a minimum of £106.50stg per test (€8,948 per year). We are in the process of identifying cases where the test did not add to the diagnostic process. In view of the high cost and low yield, better patient selection based on thorough history taking, decision rules such as the Ottawa Subarachnoid Haemorrhage rule, as well as timely CT scanning would cut down on unnecessary testing and result in significant savings.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".