Data for: Pancreatic Enzymes and Bile Acids: A Non-Antibiotic approach to Treat Intestinal Dysbiosis in Acutely Ill Severely Malnourished Children
Bibliographic record
Abstract
This dataset contains clinical information for 429 participants who were enrolled in the PBSAM trial (see protocol for more details). Participants were enrolled after meeting an inclusion criteria into the study of having at least 2 severe characteristics for an acute illness and with a severe acute malnutrition diagnosis. They were then followed up for 6 months post discharge. Data was collected in the following time-points: at admission, daily review (during hospitalization), at discharge, at 21 days after admission and finally 60 days after admission. Both clinical and some social data were collected at these time points. Clinical chemistry and biochemistry tests were conducted on samples collected in all the visits as well as blood culture and stool culture were also collected on some visits. All serious adverse events and toxicity events were recorded as well and were investigated and their data has been included here as well. Also included in the package is the Statistical Analysis Plan (SAP) for the trial, blank copies of the study questionnaires, informed consent form and data dictionary Trial Registration: www.clinicaltrials.gov NCT04542473
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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.151 | 0.030 |
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".