Study on the relationship between sublingual microcirculation disorder and pressure injury in patients with acute infection
Bibliographic record
Abstract
Key Messages• In the pathophysiological changes of acute bacterial infectious diseases, microcirculatory disorders are one of the serious consequences caused by the disease, and at the same time, they are also an important reason for the exacerbation of the disease and the deterioration of patient prognosis.• The integrity of the skin's structure and function is crucial for patients with acute bacterial infections.However, the occurrence of pressure injuries disrupts the body's effective barrier, leading to a deterioration in patient prognosis.• The occurrence of pressure injuries is closely related to the local microcirculatory perfusion status and function, and microcirculatory disorders are precisely important pathophysiological changes in patients with acute bacterial infections.• In patients with acute bacterial infections, the occurrence of pressure injuries is not only the result of various common triggering factors but also a direct manifestation of the progressive aggravation of microcirculatory disorders caused by the infection.This indirectly helps clinical healthcare personnel observe the changing trends in the condition of such patients.• By measuring the sublingual microcirculation and other methods to monitor patients, it provides clinical healthcare personnel with an operational method and means to assess the patient's condition, predict potential serious complications and adverse outcomes.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| 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".