Bibliographic record
Abstract
Dr Samlaska laments the fact that our review of therapy for warts did not emphasize the major limitations of the duct tape studies. All were small studies and, as Dr Samlaska has pointed out, the two that showed no efficacy studied clear duct tape (1,2) while the one that did show efficacy studied traditional industrial-grade duct tape (3). If the effect of duct tape is related to some component of the tape rather than to occlusion alone, this could explain the discrepancy in the results of these trials. The purpose of the Evidence for Clinicians column is to make clinicians more aware of the published evidence on common paediatric problems and to be able to provide evidence-based management options for paediatric patients. Although anecdotally Dr Samlaska and other clinicians have found duct tape alone or in combination with other therapies to be effective for warts, the limitation of this observation is that warts are self-limited and, unfortunately, there is a lack of literature backing up this observation. To resolve this issue, there is a need for large randomized trials investigating traditional duct tape. Ideally, such trials would compare standard therapy (cryotherapy or salicyclic acid using currently recommended regimens) with duct tape alone and with duct tape combined with standard therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.115 | 0.066 |
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".