Cost Implications of Reporting Nonpathogenic Protozoa
Bibliographic record
Abstract
Historically, clinical laboratories worldwide have reported intestinal protozoa, both pathogenic protozoa (PP) and nonpathogenic protozoa (NPP), to attending physicians. The majority of these organisms are nonpathogenic; they neither cause harm nor require medical therapy [1, 2]. Nevertheless, we have observed that many patients with NPP are treated and/or referred to infectious diseases specialists or gastroenterologists. At a time when health care programs, including laboratory testing, are targets for cost-cutting, it is worthwhile to re-evaluate this current policy of routinely reporting intestinal NPP. Reducing such reporting would reduce the costs of inappropriate medication, repeated stool sampling, and physician consultations that have little or no impact on health status. The present study, a survey of family physicians, was carried out to determine the number of patients infected with NPP annually in Ontario (1997 population, 11.4 million) and the costs associated with the healthcare management of these patients. The Ontario Physician Human Resource Data Centre (OPHRDC) database (1996) lists 9869 family physicians. Because only collective attributes and no individual names were available from OPHRDC, we purchased a similar database (9140 names) of Ontario family physicians from a commercial supplier. Similar surveys have yielded response rates of 50%–62.6% [3, 4]; therefore, we chose to mail 880 surveys, to yield 370 usable responses for 95% CI, ±5% error.
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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 | 0.008 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".