ASSESSING THE FREQUENCY OF CA-125 MEASUREMENTS WITHIN HAMILTON HEALTH SCIENCES AND ST. JOSEPH’S HEALTHCARE
Bibliographic record
Abstract
Objective To determined if the number and frequency of CA-125 measurements per patient reflect the 2008 NACB (National Academy of Clinical Biochemist) practice guidelines. Methods We collected data retrospectively on CA-125 over a period of one year as apart of our ongoing practice in monitoring the tumor markers for quality assurance, The number of CA-125 results per patient as well as the time interval between the 1st and 2nd measurements was noted. To enrich this population for the likelihood that the measurement of CA-125 was used for monitoring or for detection of recurrence, we divided the population into patients from the Jurvainski Cancer Centre (JCC) and the rest of the sites. Results The JCC patients contributed the majority of CA-125 results, representing over 75% of all results (n= 3057 results from 998 patients), whereas the remainder of the sites only yielded 959 results from 920 patients. Further analysis of the JCC patients indicated 2 main subgroups: Group A – patients with 1 or 2 results (n=624 patients); Group B - patients with 3 or more results , Overall, less than 4% of patients at the JCC had a time interval between the 1st and 2nd specimens of less than 2 weeks. Discussion This initial analysis would indicate that physicians are ordering CA-125 in agreement with the NACB guidelines. To further improve compliance to the guidelines and to prevent subsequent measurements of CA-125 too close, we propose restricting CA-125 orders that are less than 14 days apart to only those that receive Biochemist’s approval.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".