Impact of newer generation multidetector computed tomography on the diagnosis of abscesses in the head and neck.
Bibliographic record
Abstract
OBJECTIVE: To understand how newer generation multidetector computed tomographic (NGCT) scanner technology (≥ 16 slices) has affected the imaging characteristics of head and neck abscesses. DESIGN: Retrospective chart review. SETTING: Tertiary referral centre. METHODS: Forty-eight patients with a head and neck abscess who underwent a soft tissue neck computed tomographic (CT) scan were identified from September 1, 2001, to December 1, 2008. The degree of rim enhancement, delta (Δ), was graded using mean Hounsfield units (HU) from five peripheral points and five central points from a representative CT slice. The difference was then calculated and compared between older generation computed tomography (OGCT; < 16 slices) and newer generation multidetector computed tomography (NGCT; ≥ 16 slices) using the Student t-test. A p value < .05 was considered significant. RESULTS: Forty-eight patients met our inclusion criteria. Of these, 20 were scanned with OGCT and 28 were scanned with NGCT. The mean peripheral point values were OGCT = 78 HU (95% CI 71-86 HU), NGCT = 74 HU (95% CI 68-80 HU); p = .3. The mean central point values were OGCT = 24 HU (95% CI 21-28 HU), NGCT = 26 HU (95% CI 21-31 HU), p = 0.7. The mean delta values (mean peripheral HU--mean central HU) were OGCT = 52 HU (95% CI 43-61 HU), NGCT = 46 HU (95% CI 41-52 HU), p = .2. CONCLUSION: There is no significant difference between OGCT and NGCT in the amount of rim enhancement seen on CT scans of head and neck abscesses.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".