The effectiveness of the Canadian triage guidelines in improving the treatment outcomes of cancer patients with febrile neutropenia
Bibliographic record
Abstract
Background: Febrile neutropenia (FN) is among the most common side effects related to \ncancer treatment and a significant cause of morbidity and mortality. Patients with FN are \nat a higher risk of developing life-threatening sepsis without prompt treatment. \nPurpose: To evaluate the quality of emergency care of cancer patients with FN. \nSpecifically, the study aimed to examine the effectiveness of triage on select treatment \noutcomes for patients with FN as specified by the Canadian triage guidelines. \nMethods: A retrospective cohort design was employed to collect data over five years from \nthe emergency department (ED) records of all adult cancer patients with FN in one urban \nhealth care organization in Atlantic Canada. \nResults: The Canadian triage guidelines identify the acuity and urgency of FN. The \nguidelines, however, do not translate well in practice as two-thirds of the patient sample \nwere inappropriately triaged (mal-triaged) to less urgent triage categories. Mal-triage was \nsignificantly associated with delayed times for physician initial assessment, \nadministration of antibiotics, and decision about admission. Additional factors that \ncontributed to the quality of ED care of cancer patients with FN were also examined. \nConclusion: The results of our quality evaluation provided evidence that improvements in \na number of the quality dimensions have the potential to enhance the care provided to \nindividuals with FN. Our results established an initial understanding of the factors that \ninfluence the mal-triage of patients with FN. Improving triage decision-making is an \nessential first step but this will not completely improve the quality of care in the ED for \nclients presenting with FN until problems in other parts of the system are resolved to \naddress the health care outcomes and needs of this vulnerable population.
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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 | 0.004 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".