Bibliographic record
Abstract
In the current issue of Paediatrics & Child Health, Beringer et al (pages 23–29) report the results of a review of medical records investigating the use of natural health products (NHPs) by children in a hospice in Ontario between 2008 and 2013. The authors have demonstrated a very high rate of use of NHPs, with three-quarters of the children receiving at least one NHP in addition to conventional therapy. This finding is higher than in previous studies investigating NHP use in Canadian children; for example, the work of Adams et al (1) which demonstrated a rate of 37% among children with chronic health problems attending specialty clinics in Ontario and Alberta. The reasons for this may lie in the nature of the patients; as the authors note, the children in this study have complex disease with limited life expectancy. These figures are also closer to those observed among adult patients with cancer, in whom up to 60% use some type of NHP (2). An additional important finding in this study is that there were a number of potential drug-NHP or NHP-NHP interactions, which were present among 66% of all patients. Of interest, there were no clinically relevant drug-NHP interactions reported during the study period. This observation should be interpreted with some caution because under-reporting of adverse events is common in records reviews, and adverse events may not have been as well appreciated in the setting of hospice care. What is the take-home message of this article for the clinician? First, that use of NHPs is likely to be common among children, especially among children with complex and serious disease. Second, that some of this NHP use may have the potential for producing interactions not only with concurrent drug therapy but also with other NHPs. This highlights the importance of a careful history, with clinicians being nonjudgemental with regard to NHP use to encourage candor on the part of children and their parents. In this respect, pharmacy team members can be a vital resource, particularly with respect to medication history at the time of hospital admission or clinic visit. In addition, clinicians need to take advantage of drug information resources to ensure that they have timely and accurate information with regard to potential drug-NHP and NHP-NHP interactions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".