RETRACTED: <scp>Meta‐analysis</scp> of the impact of laser interstitial hyperthermia on wound healing complications in brain tumors
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Abstract High‐grade gliomas (HGGs) may be amenable to the neurosurgical technique known as laser interstitial thermal therapy (LITT), which delivers thermal energy to interstitial brain injuries and wounds with pinpoint accuracy. The purpose of this extensive meta‐analysis was to evaluate the effects of LITT on wound complications among patients who have brain tumours. Diverse conclusions emerge from a systematic review of pertinent studies, necessitating a comprehensive examination. The meta‐analysis, performed utilizing the meta library provided by the R package meta, reveals an initial significant overall effect (RR: −2.1262, 95% CI [−2.7466, −1.5059], p < 0.0001) accompanied by considerable heterogeneity among studies ( I 2 = 61.13%). Following analyses that specifically examined the incidence of wounds, a complex correlation was found (RR: 0.0471, 95% CI [0.0264, 0.0842], p < 0.0001), indicating that LITT has a discernible but insignificant effect on the occurrence of wounds. Although the meta‐analysis emphasizes a notable decrease in wound complications subsequent to LITT treatment, additional research is warranted due to constraints in standardized reporting, data accessibility, and small sample sizes. The results of this study underscore the need for exhaustive protocols to analyse wound complications in patients with brain tumours undergoing LITT.
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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 | 0.023 | 0.133 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.013 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.054 | 0.007 |
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".