Bibliographic record
Abstract
Purpose. Antineoplastic agents have a narrow therapeutic index and therefore require the greatest degree of dose individualization. In choosing a body surface area (BSA) for‘mula, the challenge remains in obtaining consensus among prescribers. The objective of this review is to assess the various formulae available for calculating BSA and to adopt a standard formula within an institution. Methods. Retrospective review of medication orders containing the patient’s height and weight as well as BSA calculated by the outpatient clinic staff within the Cross Cancer Institute (CCI) from 7 to 15 July 1998. Four BSA calculating devices, in current use by the clinical staff, were also tested to determine the formula currently utilized by prescribers. Results. Thirty-three orders sent to the pharmacy containing the patient’s height, weight, and BSA were reviewed. Ninety-four per cent of the BSA calculated by the clinic staff differed from pharmacy’s calculations by 0% to 4%. One BSA calculation differed by 8% and another differed by 14%. With respect to the available BSA calculating devices, all appeared to be based on the Du Bois formula. Conclusion. Of the various formulae available for estimating BSA, we concluded that the Mosteller equation is the most acceptable based on both BSA calculation accuracy and ease of use. Based on this determination, the CCI approved the adoption of the Mosteller equation as the institutional standard. This equation was later standardized throughout the entire Alberta Cancer Board. Other methods of dose determination are emerging, including therapeutic drug monitoring and PMT dosing. Further investigation is required as to whether these methods will lead to better standardization of dose.
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 | 0.033 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".