FRUSTRATIONS WITH FIDELIS: PROMISING IDEA, PROBLEMATIC APPROACH
Bibliographic record
Abstract
Despite increased funding over the last decade, global tuberculosis (TB) control efforts have fallen short of their intended case detection targets.1 The FIDELIS initiative2 could help to close the gap by funding locally developed, innovative solutions that go beyond the current paradigm of passive case finding.3 Unfortunately, although FIDELIS’s overall objective is worthy, the approach is inadequate. First, it is not clear how much room for local, “innovative” proposals actually exists in the FIDELIS framework. Though the initiative purports to engage in-country contractors, its Web-based call for applications and its minimum proposal request of US$150000 exclude grassroots groups with low Internet access and limited capacity to manage large funds. Thus, FIDELIS is most accessible to organizations with significant capacity (i.e., those with affiliates in developed countries), whose proposals may be influenced by outside notions of “innovation” that are not locally relevant. Second, proposals are evaluated by experts within the existing TB control structure (Stop TB Partnership, National Tuberculosis programs, etc.), whose involvement in existing standardized strategies may limit their endorsement of new approaches. A shift toward innovative, context-specific strategies is key to improved TB control,4,5 but the FIDELIS framework is not conducive to realizing this goal. Third, the 2 key principles of FIDELIS’s proposal evaluation process contradict each other. To “focus on people with limited access to health services,”2 it may be necessary to violate the stringent cost-per-treatment-success target of US$80. Isolated populations are often underserved precisely because of the higher fixed costs inherent in reaching them (i.e., greater transportation expenditures). By imposing a cost-per-cure ceiling, FIDELIS favors urban and periurban locales. Focusing primarily on cost-effectiveness can be shortsighted and will ultimately hinder progress toward global TB control.6 Finally, the proposed funding cycle is problematic. The quick disbursement of funds and short project cycle may seem desirable in principle, but the 1-year funding cycle leaves little time to build project capacity before activities are expected to begin. Projects delayed by long waits for government approval could receive negative evaluations. The 8- to 10-month delay between the funding cycle’s end and the availability of TB treatment outcomes2 may leave projects in limbo. Increasing the contract length to 2 years, even without increased funding, might be beneficial for all parties. FIDELIS is a promising concept, but one that requires an improved approach to achieve the desired results.
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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 | 0.199 | 0.238 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.028 | 0.080 |
| Scholarly communication | 0.030 | 0.042 |
| Open science | 0.010 | 0.026 |
| Research integrity | 0.031 | 0.061 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".