Bibliographic record
Abstract
Peri-operative care of paediatric patients presents unique challenges and opportunities for surgeons and anaesthetists to work synergistically. No subpopulation of patients is more heterogeneous with respect to physiology or spectrum of pathology. Pre-operative Having decided that the child before them requires an operation, surgeons must consider a number of areas peculiar to paediatric patients. Need for general anaesthesia Many procedures that would be done in adults under local anaesthesia, or with conscious sedation, cannot be achieved without general anaesthesia in children. Examples include MR imaging studies, GI endoscopies and most minor body surface surgery. Assessment and optimisation Most elective procedures in children are performed on a day case basis. Prudent selection and referral of patients who require pre-operative anaesthetic assessment for optimisation is critical, to avoid both unnecessary additional hospital visits and day of surgery cancellations. Planned pre-operative admission This is an indication for a detailed pre-operative anaesthesia assessment. The admission is likely a function of some combination of the magnitude of surgery, existing co-morbidity and the need for advanced post-op pain management modalities. Adequate time needs to be provided for optimisation of co-morbidities, and for the risks and benefits of anaesthesia and post-op pain management strategies to be presented and digested by the patient and family.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.013 |
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".