Use of a treatment protocol in the management of community-acquired lower respiratory tract infection
Bibliographic record
Abstract
The aim of the present study was to examine the impact of an antimicrobial prescribing protocol on clinical and economic outcome measures in hospitalized patients with community-acquired lower respiratory tract infection (LRTI). The study was performed as a prospective controlled clinical trial within the medical wards at Antrim Area Hospital, Northern Ireland. Data were collected on all hospitalized adult patients with a primary diagnosis of LRTI during the period December 1994 to February 1995 (normal hospital practice; control group; n = 112). After an LRTI management protocol (medical, microbiological and pharmacy staff) had been developed, all hospitalized adult patients with a primary diagnosis of LRTI over the period December 1995 to February 1996 formed the intervention group (treated according to the protocol; n = 115). The results showed a statistically significant impact of the protocol in terms of clinical and economic outcome measures. Patients treated using the algorithmic prescribing protocol had significant reductions in length of hospital stay (geometric mean 4.5 versus 9.2 days), iv drug administration (34.8% versus 61.6%), duration of iv therapy (geometric mean 2.1 versus 5.7 days) and treatment failures (7.8% versus 31.3%). Healthcare costs were also significantly reduced. The use of the protocol was a major factor in streamlining the prescribing of antimicrobial therapy for community-acquired LRTI and led to more cost-effective patient management.
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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 | 0.022 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".