Bibliographic record
Abstract
Sir—I wish to respond to the letter by Sánchez et al. [1] that described acalculous cholecystitis (AC) in association with Plasmodium falciparum malaria. There are important differences between their case and the one that Dr. Al-Azragi and I reported [2]. In the case reported by Sánchez et al. [1], the results of multiple smears for malaria parasites were negative at the time of presentation of AC. Treatment with broad-spectrum antibiotics resulted in a clinical and radiological response, and at the time of the patient's readmission to the hospital, a bacterial pathogen was isolated from the bloodstream. Demonstration of the malaria infection occurred only after the AC had resolved. This clearly contrasts with the case that Dr. Al-Azragi and I reported [2], in which the patient failed to respond to broad-spectrum antibiotics and in which no bacterial pathogen was isolated from the blood cultures. Clinical and radiological resolution occurred only after administration of specific therapy for P. falciparum. I believe that the facts associated with the case presented by Sánchez et al. suggest that the cause of the AC was a Shigella infection that relapsed after treatment. The fever that persisted, despite antibiotic therapy, at the time of the patient's readmission to the hospital was most likely caused by malaria.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.035 | 0.033 |
| Insufficient payload (model declined to judge) | 0.009 | 0.010 |
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".