Pneumocystis pneumonia in patients with inflammatory or autoimmune diseases: Usefulness of lymphocyte subtyping
Bibliographic record
Abstract
Objectives No consensus currently exists on the indications for Pneumocystis jirovecii prophylaxis in patients with inflammatory or autoimmune diseases. The main objective was to identify biomarkers associated with P. jirovecii pneumonia (PCP) in this population. Methods A retrospective study was carried out at Beijing Union Medical College Hospital (2003–2014). All patients with an inflammatory or autoimmune disease presenting with acute onset of fever and respiratory symptoms were included. Results A total of 123 patients were included, of whom 42% had confirmed PCP, 18% had possible PCP, and 40% were negative for PCP. Immunosuppressive conditions consisted mostly of diffuse connective tissue disease (50%) and primary nephropathy (20%). Immunosuppressive therapies consisted of corticosteroids (95%) with concomitant non-steroidal drugs (80%). Independent predictors of PCP were a CD3+ cell count <625×10 6 /l, serum albumin <28g/l, and PaO 2 /FiO 2 <210. Furthermore, 90% of patients with PCP had a CD3+ cell count <750×10 6 /l. Independent predictors of mortality were a CD8+ cell count <160×10 6 /l and a PaO 2 /FiO 2 <160. Conclusions In patients with inflammatory and autoimmune conditions receiving immunosuppressive therapy, low CD3+ and CD8+ cell counts were strongly associated with PCP and its mortality. These results suggest that lymphocyte subtyping is a very useful tool to optimize the selection of patients needing prophylaxis.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".