Coverage and Appropriateness of the Taiwan Adult Triage Complaint List
Bibliographic record
Abstract
Background: Our purpose was to evaluate the coverage and comprehensiveness of the Taiwan Triage System (TTS) adult complaint list. Method: We retrospectively collected all triage chief complaints of adult patients admitted to the emergency departments of 4 hospitals in one medical system in January 2004. Their complaints were classified according to the TTS adult complaint list and the Canadian Emergency Department Information System (CEDIS) complaint list. The percentages of complaints classified by each system were calculated to compare their coverage. Those complaints that could not be classified by the TTS adult complaint list were reorganized according to the CEDIS complaint list categories. The criteria which were not used in the Taiwan system were also analyzed for their appropriateness. Results: There were 24472 complaints enrolled in our study. The TTS adult complaint list only covered and classified 43.06% of all complaints, 40.96% of non-trauma complaints, and 58.78% of trauma complaints. The TTS adult complaint list was inappropriate and not comprehensive for the following reasons: (1) Some criteria on the list were diagnoses instead of complaints (2) Ophthalmic, otorhinolaryngologic, and dental complaints were not included. (3) Many common emergency complaints were not included. The CEDIS complaint list covered and classified 98.72% of all complaints, 55.66% more than the TTS adult complaint list. Conclusion: The TTS adult complaint list classified less than half of complaints. We suggest that the Emergency Medicine expert panel revise the current TTS complaint list and develop a more comprehensive set of complaints in order to increase coverage and generate more reliable triage classifications.
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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 | 0.007 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".