Bibliographic record
Abstract
Sir, It was a pleasure reading the review article on parenteral nutrition (PN).[1] We congratulate the authors for their endeavor in making the topic so interesting. However, we feel that a couple of points need further discussion. As per the guidelines for the provision and assessment of nutrition support therapy in adult critically ill patient set by the Society of Critical Care Medicine (SCCM) and American Society for Parenteral and Enteral Nutrition (A.S.P.E.N), 2009, serum protein markers (albumin, prealbumin, transferrin, C-reactive protein) are not recommended for determining adequacy of protein provision.[2] In all ICU patients receiving PN, initial mild ‘permissive underfeeding’ (providing approximately 80% of the total energy requirement) should be considered as it has been proved that excessive energy intake can lead to insulin resistance, greater infectious morbidity, extended mechanical ventilation and increased hospital length of stay. Eventually, as the patient stabilizes, PN may be increased to meet energy requirements.[2] The Canadian Critical Care Clinical Practice Guidelines’2003 states that there are insufficient supportive data to make a recommendation regarding parenteral Selenium supplementation in critically ill patients.[3] This has again been emphasized in the society’s 2009 guidelines. Finally, in patients stabilized on PN, periodically repeated efforts should be made to initiate enteral nutrition (EN).[2] As tolerance improves and the volume of EN calories delivered increases, the amount of PN calories supplied should be reduced. PN should not be terminated, until ≥60% of target energy requirements are being delivered by the enteral route.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.022 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".