Patients who Talk and Deteriorate: A New Look at an Old Problem
Bibliographic record
Abstract
BACKGROUND AND METHODS: We sought to review established prognostic indicators applied to Asian population, and to identify new risk factors for deterioration in patients who talked and deteriorated after traumatic brain injury (TBI). This retrospective study used our prospectively maintained TBI database. From August 1999 to July 2001, 324 patients were admitted to the neurosurgical intensive care unit (ICU). Thirty-eight patients (11.8%) talked between injury and subsequent deterioration into coma. Independent outcome predictors were studied. RESULTS AND CONCLUSION: Fourteen patients had subdural haematomas, 9 extradural haematomas, 19 contusions/haematomas and 3 subarachnoid haemorrhages. 81.5% of the patients had mass lesions potentially requiring surgery. Twenty patients had good functional recovery at 6 months (Glasgow Outcome Score 4 and 5); 18 were dead or vegetative. Age, gender, type of intracranial lesion and presence of coagulopathy were significantly correlated with outcome. Intracranial haematomas continue to be most significant in patients who talk and deteriorate. Coagulopathy was the strongest prognostic predictor of poor outcome with fibrinolytic parameters being reliable prognostic markers of head injury. Early identification, continued monitoring and treatment of coagulopathy should be our new look at improving outcome of these patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".