PO-0284 Use Of Soft Tissue Neck Radiographs In Paediatric Acute Airway Obstructions: Current Perspective
Bibliographic record
Abstract
Background Soft tissue neck radiographs (STNRs) have been recognised as a helpful tool to differentiate benign causes of acute upper airway obstructions (AUAO) from epiglottitis, retropharyngeal abscess or foreign body aspiration. Based on the frequency at which those life-threatening pathologies now occur, are STNRs still widely used and what is the impact of their clinical use? Methodology A retrospective study was conduct. Medical files from children aged 0–17 who had a STNR between January 1st 2010 and December 31st 2011 in a mid-size Canadian university paediatric hospital were reviewed. Patients were divided in 2 groups according to the conditions clinicians first suspected and compared the STNRs’ interpretations with Fisher exact test. Cases of epiglottitis and retropharyngeal abscess for the same period were reviewed. Results Among 520 STNRs identified, 88% intended to evaluate AUAO among children. Nearly all (99%) took place at the emergency room, with 73% of patients presenting with triage score 3 and above, and 49% necessitating admission. Most STNR were performed among males (67%) aged below 5 y.o. (66%; mode=1 y.o.). A life-threatening cause of AUAO was first suspected by clinicians among 40% of reviewed charts. Convincing radiologic signs of life-threatening pathologies were found in 40 children (9%). Overall, 2 cases of epiglottitis and 8 retropharyngeal abscesses were diagnosed during the study period. Conclusion STNR is a radiologic study that is frequently used among children. The current value of STNRs appears uncertain as few showed solid evidences that may modify how clinicians currently manage paediatric AUAO.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".