Metallothionein expression in patients with small cell carcinoma of the lung: correlation with other molecular markers and clinical outcome.
Bibliographic record
Abstract
BACKGROUND: Patients with small cell carcinoma of the lung (SCLC) are known to have an extremely poor prognosis, with a 5-year survivor rate of only 5%. Chemotherapeutic drug resistance is a major obstacle to curative therapy in patients with SCLC. METHODS: The authors evaluated retrospectively the expression of metallothionen (MT), proliferating cell nuclear antigen (PCNA), p53, and retinoblastoma gene product (RBGP) in biopsy samples from 58 patients with SCLC prior to standard chemotherapy. The objective was to study the correlation between MT and other molecular markers in SCLC and correlate these data with the clinical outcome of patients. The authors studied 28 short-term survivors (STS; survival < 24 months) and 30 long-term survivors (LTS; survival > 24 months). RESULTS: In line with expectations, the authors found a strong inverse association between stage and survival. Of 58 patients with SCLC, 26 patients (45%; 17 STS and 9 LTS) showed MT expression, 55 patients (94%; 28 STS and 27 LTS) were positive for PCNA, 28 patients (48%; 16 STS and 12 LTS) were positive for p53, and only 6 patients (10%; 1 STS and 5 LTS) showed positivity for RBGP. On comparing the percent positivity of various markers in the two survivor groups, there was greater frequency of expression of MT, PCNA, and p53 and lower RBGP expression in the STS group compared with the LTS group. However, only the difference in expression of MT between the two survivor groups was statistically significant (Fisher exact test; P = 0.034). Multivariable analysis using a logistic regression model showed a significant association between MT expression and patient survival after adjusting for disease stage (chi-square test; P = 0.022). There was also a statistically significant association between MT expression and p53 expression (chi-square test; P = 0.001). CONCLUSIONS: In this study, of the molecular markers studied, the authors demonstrated that only MT overexpression was independently predictive of short-term survival in patients with SCLC undergoing chemotherapy.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".