The Utility of Paraneoplastic Serology in the Saskatoon Health Region (P5.021)
Bibliographic record
Abstract
OBJECTIVE: To determine the diagnostic yield of paraneoplastic serological markers tested for in the Saskatoon Health Region; the discrepancy between the tests ordered and processed; and the clinical characteristics of the patients for which they are ordered. BACKGROUND: Paraneoplastic neurological disorders (PNDs) are syndromes of protean presentation, the most classic being cerebellar degeneration. PNDs can be associated with numerous antibodies, but not in a 1-to-1 relationship. Hence, PNDs can be included in many differentials, and often multiple antibodies are tested for; however, PNDs are still difficult to identify and confirm. As these disorders may manifest before the diagnosis of a cancer, they are often considered in the differentials of patients without an oncological history. While, the understanding of PNDs is growing, there are no clear guidelines of when they should be worked up, and to what extent. DESIGN/METHODS: We identified all patients who were admitted under, or consulted to, the neurology service between Jan 1, 2012 and May 1, 2013; and for whom paraneoplastic serology was ordered. For each patient identified we applied a data collection instrument to assess the serology ordered, the serology processed, the results and the clinical characteristics of the presentation. RESULTS:In the 17 month span, 32 patients were screened for paraneoplastic serology, of which 2 (6.25%) has positive results. 278 serological tests were ordered, in total, of which 2 (0.72%) were positive. 17/32 (53%) cases had either errors of omission or commission, in terms of the serological tests ordered and those processed. 3/32 (9.4%) patients had an alternative diagnosis for their symptoms, of the remainder, 3/29 (10.3%) had evidence of a distal cancer. CONCLUSIONS:The positive result rate, per patient, for any PND workup was 6.25%. This translates to about 0.72% per individual test. Of patients with a proven distal malignancy, the positive result rate per patient was 33%. There was a discrepancy between the serology ordered, and that processed, in 53% of the cases.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".