Referee report. For: A review of the Cochrane COVID-19 Study Register reveals inconsistency in the choice and measurement of SARS-CoV-2 infection outcomes in prevention trials [version 1; peer review: 1 approved, 1 approved with reservations]
Bibliographic record
Abstract
Background: Multiple studies are evaluating how to prevent SARS-CoV-2 infection. Interventions are wide ranging and include vaccines, prophylactic drugs, public health safety measures, and behavioural interventions. Heterogeneity in the outcomes measured and reported is leading to research waste and inefficiency, slowing worldwide identification and implementation of effective methods to prevent infection. A core outcome set (COS) for studies of interventions to prevent SARS-CoV-2 infection has recently been developed, identifying infection as a critical outcome to measure. This paper examines how SARS-CoV-2 infection outcomes are measured in registered COVID-19 prevention trials and considers how this can be improved. Methods: We searched the Cochrane COVID-19 Study Register to identify and review SARS-CoV-2 infection outcomes in prevention trials, including the rationale for choice of outcome measurement. We included phase 3 and 4 trials of COVID-19 prevention interventions. Early phase trials and studies relating to the transmission, treatment or management of COVID-19 were excluded. Results: We identified 430 entries in the register, of which 199 unique prevention trials were included across eight settings and 12 intervention types. Fifteen (8%) trials did not include any SARS-CoV-2 infection outcomes. The remaining 184 (92%) studies included a total of 268 SARS-CoV-2 infection outcomes, of which 32 (17%) did not specify how infection would be measured. Testing (i.e. formal diagnostic test) as a standalone method for determining infection was used in 57 (31%) trials, whereas defining infection by symptoms alone was used in 16 (9%) trials. All other trials (n=79, 43%) included multiple infection outcomes, defined in different ways. Discussion: There is considerable variation in how SARS-CoV-2 infection is measured within and across different interventions and settings. Furthermore, few studies report the rationale for outcome selection and measurement. Better transparency and standardisation of SARS-CoV-2 infection measurement is needed for the findings from prevention trials to inform decision-making.
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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 | 0.036 | 0.452 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.022 | 0.009 |
| Insufficient payload (model declined to judge) | 0.345 | 0.121 |
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".