34 Pharmacotherapy-related practices and skills as reported by attendees of the neonatal online training and education (NOTE) course on neonatal clinical pharmacology
Bibliographic record
Abstract
Introduction We intended to get a snapshot of pharmacotherapy-related practices at the initiation of the 2022 clinical pharmacology NOTE course. 1 Besides guiding education, this also informs us of contemporary practices, research or additional needs of early career colleagues.Methods The online questionnaire was developed by the NOTE content coordinator of the pharmacology module (KA), subsequently distributed to the NOTE pharmacology faculty for input. Topics covered were: (1)Who do you contact with drug-related questions? (2)Does a pharmacist visit your unit? (3)Have you ever reported an adverse drug reaction (ADR)? (4)What information sources do you use? (5)Have you ever assisted in a trial as (co)investigator? (6)What are the top 5 prescribed drugs in your unit?Results Fifty three (77%) responses were received from 69 participants (United Kingdom 15, Qatar 7, India 5, Trinidad/Tobago 5, Ireland 5, Emirates 5, Palestinian Authority 4, Norway 4, Nigeria 3, Denmark 2, Canada 2, Zambia 2, Mauritius 2, and Bahrain, Japan, Kenya, Iceland, Indonesia, Malta, Portugal, United States), mainly doctors (64). On Q1, colleague-neonatologists (39), external sources/formularies (5), or pharmacists (9) were mentioned. Q2: 27 (39%) reported regular/daily visits of a pharmacist, 9 weekly, 17 had no structured visits. Q3: 17 (32%) have reported an ADR on at least one occasion. Q4: Information sources (formulary) used by the respondents were BNFc (22), NeoFax (through Micromedex) (21) Neonatal Formulary (3) or Lexicomp (through UpToDate) (5). Others (25) mentioned local, regional or national formularies. Q5: Six respondents had assisted at least in one drug research trial. Q6: The top 5 drugs prescribed by the respondents were caffeine, gentamicin, benzylpenicillin, ampicillin, amikacin. Discussion This snapshot reflects large heterogeneity in practices (who to consult for drug-related questions, access to pharmacist, information sources) and experiences (ADR reporting, trial involvement), and provides guidance on needs (pharmacists, ADR reporting, information sources awareness) on pharmacotherapy. The top 5 drugs reflect the common use of antibiotics. Conclusions The results of this questionnaire reflect needs to improve our practices, by raising awareness of formularies, ADR reporting or a broader involvement in clinical trials. Questionnaires are useful to learn from and interact with NOTE participants.Reference https://moodle.neonataltraining.eu/
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".