Pulmonary function in children post-empyema: spirometry <i>versus</i> lung clearance index
Bibliographic record
Abstract
Background We hypothesise that the lung clearance index (LCI) would be superior to spirometry in diagnosing early-stage respiratory diseases earlier and, more precisely, in patients who received medical or surgical treatment for empyema. Methods Children over 5 years old diagnosed with empyema at least 6 months ago were recruited. In addition, a control group was created from healthy individuals between the ages of 5–18 years. Spirometry and LCI were performed in both groups. Results The spirometric values of the patients were compared with the spirometric values of the controls; there was no significant difference between the patient and control groups' forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC) and FEV1/FVC z scores results when compared (p=0.610, p=0.342 and p=0.298, respectively). In addition, when the LCI 2.5% values of the patients were compared with the LCI 2.5% values of the controls and reference values, the LCI 2.5% was found to be significantly abnormal (p=0.003 and p=0.005, respectively). Conclusion In the long-term follow-up of patients who received inpatient treatment for empyema, airway disease that could not be detected by spirometry was obtained using the LCI method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".